i want to do poster presentation behavioural profile of children with late diagnosis due to subtle features of ASD clicically diagnosed and ISSA scores are not diagnostic. sent you my sheet . tell me how should i draw results out of this

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python - <<'PY' from openpyxl import load_workbook p='/tmp/workspace/attachments/bc8e6272-90da-4017-92b5-e411ef937585/POSTER PRESENTATION.xlsx' wb=load_workbook(p, data_only=False) print('SHEETS:', wb.sheetnames) for ws in wb.worksheets: print('\nSHEET',ws.title,'rows',ws.max_row,'cols',ws.max_column) for r in ws.iter_rows(min_row=1,max_row=min(ws.max_row,15),values_only=True): print(r) PY

Running Command

python - <<'PY' import pandas as pd, re from pathlib import Path p=Path('/tmp/workspace/attachments/bc8e6272-90da-4017-92b5-e411ef937585/POSTER PRESENTATION.xlsx') df=pd.read_excel(p) def age_m(s): s=str(s).lower(); y=re.search(r'(\d+)\s*year',s); m=re.search(r'(\d+)\s*m',s) return (int(y.group(1))*12 if y else 0)+(int(m.group(1)) if m else 0) df['age_months']=df['AGE'].map(age_m) print('n',len(df)); print('\nSex\n',df.SEX.value_counts()) print('\nAge months',df.age_months.tolist(),df.age_months.mean(),df.age_months.median(),df.age_months.min(),df.age_months.max(),df.age_months.std(ddof=1)) print('\nISSA',df.ISSA.tolist(), df.ISSA.mean(),df.ISSA.median(),df.ISSA.min(),df.ISSA.max(),df.ISSA.std(ddof=1)) print('\nFSIQ',df.FSIQ.tolist(), df.FSIQ.mean(),df.FSIQ.median(),df.FSIQ.min(),df.FSIQ.max(),df.FSIQ.std(ddof=1)) print('\nVSMS',df['VSMS SQ'].tolist(), df['VSMS SQ'].mean(),df['VSMS SQ'].median(),df['VSMS SQ'].min(),df['VSMS SQ'].max(),df['VSMS SQ'].std(ddof=1)) for col in ['INTERNALIZING','EXTERNALIZING']: print('\n',col,df[col].value_counts().to_dict(),(df[col].value_counts(normalize=True)*100).round(1).to_dict()) # exact CBCL domain any mentions for key in ['WITHDRAWN','SOCIAL','ATTENTION','ANXIOUS','DEPRESSED','SOMATIC','AGGRESSIVE']: print(key, df.CBCL.str.contains(key,case=False,na=False).sum()) # complaints keyword and categories exact print('\ncomplaints',df['PRESENTING COMPLAINTS'].tolist()) for key in ['ACADEMIC','BEHAV','SOCIAL','SPEECH']: print(key,df['PRESENTING COMPLAINTS'].str.contains(key,case=False).sum()) # scores by burden categories for col in ['INTERNALIZING','EXTERNALIZING']: print('\n',col,df.groupby(col).agg(n=('ISSA','size'), issa=('ISSA','mean'),fsiq=('FSIQ','mean'),vsms=('VSMS SQ','mean')).round(1)) print('\nCorrelation Spearman') print(df[['ISSA','FSIQ','VSMS SQ','age_months']].corr(method='spearman').round(2)) PY

What your sheet can show

You have a small descriptive clinical case series of 11 clinically diagnosed children with ASD, whose diagnosis was delayed because early features were subtle and whose ISSA scores were not diagnostic.

Do not present it as a diagnostic-accuracy study, because there is no comparison/control group and no sensitivity/specificity data. The strongest poster message is:

In this clinically diagnosed late-identified ASD sample, ISSA scores were largely below the diagnostic threshold, while behavioural profiling commonly showed withdrawal, social difficulties, attention problems, and internalizing symptoms. Cognitive ability was generally average, whereas adaptive functioning was relatively lower.

This fits your stated clinical observation well. Screening instruments are not diagnostic in isolation and must be interpreted alongside developmental history and clinical observation, as CDC guidance explains.

1. Results from your current data

Sample profile

Variable	Result
Total sample	11 children
Male	6 (54.5%)
Female	5 (45.5%)
Age range	7 years 1 month to 15 years 4 months
Mean age	11.0 years
Median age	11 years 3 months
ISSA score, mean ± SD	67.5 ± 2.2
ISSA score, median (range)	68 (62-70)
FSIQ, mean ± SD	95.5 ± 9.5
FSIQ range	80-111
VSMS Social Quotient, mean ± SD	79.9 ± 10.6
VSMS Social Quotient range	60-100

Key point to highlight

All children had ISSA scores ranging from 62 to 70. If the usual diagnostic cutoff in your local ISSA protocol is ≥70, then:

10/11 children (90.9%) were below the cutoff.
1/11 (9.1%) was at the cutoff.
Yet all 11 were clinically diagnosed with ASD.

Use the exact ISSA cutoff recommended by the version/manual used at your centre. Do not state “ISSA-negative” unless you document the cutoff in Methods.

2. Behavioural results

CBCL syndrome-scale findings

CBCL behavioural feature	n	%
Withdrawn behaviour	7	63.6%
Social problems	7	63.6%
Attention problems	5	45.5%
Depressive features	4	36.4%
Anxious symptoms	3	27.3%
Somatic complaints	2	18.2%
Aggressive behaviour	2	18.2%

Internalizing and externalizing domains

CBCL domain	Normal n (%)	Borderline n (%)	Clinical n (%)
Internalizing	6 (54.5%)	2 (18.2%)	3 (27.3%)
Externalizing	7 (63.6%)	3 (27.3%)	1 (9.1%)

For a poster, combine borderline + clinical as “elevated”:

Elevated internalizing symptoms: 5/11 (45.5%)
Elevated externalizing symptoms: 4/11 (36.4%)

Interpretation

Your profile suggests that internalizing difficulties were more prominent than overt externalizing symptoms. The most frequent behavioural descriptors were:

Withdrawal
Social problems
Attention problems

This is a clinically meaningful result for subtle or late-recognized ASD, because children may present with anxiety, withdrawal, school difficulty, behaviour concerns, or nonspecific social problems rather than an obvious autism presentation.

3. Presenting complaints

Because several children have more than one presenting complaint, present this as multiple-response data. Percentages will not add to 100%.

Presenting complaint category	n	%
Behavioural concern	6	54.5%
Academic difficulty	5	45.5%
Social difficulty	3	27.3%
Speech/language concern	2	18.2%

Important interpretation

Only 3 children (27.3%) presented primarily with social concerns and only 2 (18.2%) with speech concerns, whereas behavioural and academic complaints predominated. This supports your poster theme that ASD can be missed when the initial referral concern is not explicitly autism-related.

4. Best graphs for the poster

Use three simple figures, not too many.

Figure 1: Presenting complaints

Graph: Horizontal bar chart
Title: Presenting complaints among children with late clinically diagnosed ASD (n = 11)

Bars:

Behavioural concern: 54.5%
Academic difficulty: 45.5%
Social difficulty: 27.3%
Speech/language concern: 18.2%

Add under graph:
Categories are not mutually exclusive.

Figure 2: CBCL behavioural profile

Graph: Horizontal bar chart
Title: Frequency of behavioural problems on CBCL syndrome scales

Bars:

Withdrawn: 63.6%
Social problems: 63.6%
Attention problems: 45.5%
Depressed: 36.4%
Anxious: 27.3%
Somatic complaints: 18.2%
Aggressive behaviour: 18.2%

This should be your main visual, because it communicates the behavioural phenotype immediately.

Figure 3: Internalizing versus externalizing symptoms

Graph: 100% stacked bar chart with three colours:

Normal
Borderline
Clinical

Two bars:

Internalizing: 54.5% normal, 18.2% borderline, 27.3% clinical
Externalizing: 63.6% normal, 27.3% borderline, 9.1% clinical

Alternative simpler chart: show “elevated” category only:

Internalizing elevated: 45.5%
Externalizing elevated: 36.4%

5. A useful table for cognitive and adaptive profile

This is potentially a strong clinical observation.

Measure	Mean ± SD	Median	Range
FSIQ	95.5 ± 9.5	97	80-111
VSMS Social Quotient	79.9 ± 10.6	79	60-100

Interpretation

The mean FSIQ was in the broadly average range, while mean adaptive social functioning was lower. Phrase this carefully:

“The sample showed broadly average cognitive ability (mean FSIQ 95.5) with relatively lower adaptive social functioning (mean VSMS Social Quotient 79.9).”

Do not claim a statistically significant FSIQ-VSMS difference unless you conduct and report an appropriate paired statistical test and your scale manuals support this interpretation. With only 11 children, use this as a descriptive observation.

6. Suggested poster Results text

You can use this almost directly:

Eleven children with clinically diagnosed ASD who had late recognition due to subtle clinical features were included. The mean age was 11.0 years (range: 7 years 1 month to 15 years 4 months); 6 (54.5%) were male. ISSA scores ranged from 62-70, with a mean score of 67.5 ± 2.2. Ten children (90.9%) scored below the locally used ISSA diagnostic cutoff of 70, despite a clinical diagnosis of ASD.

Behavioural and academic concerns were the most frequent presenting complaints, reported in 6 (54.5%) and 5 (45.5%) children respectively. Social concerns and speech concerns were documented in 3 (27.3%) and 2 (18.2%) children respectively.

On CBCL profiling, withdrawn behaviour and social problems were each present in 7 children (63.6%), followed by attention problems in 5 (45.5%). Elevated internalizing symptoms, defined as borderline or clinical range, were observed in 5 children (45.5%), compared with elevated externalizing symptoms in 4 children (36.4%). Mean FSIQ was 95.5 ± 9.5 and mean VSMS Social Quotient was 79.9 ± 10.6.

7. Suggested conclusion

Children with subtle ASD features may present late with behavioural, academic, emotional, or attention-related concerns rather than a primary complaint of social-communication difficulty. In this small clinically diagnosed sample, ISSA scores were commonly below the diagnostic cutoff, supporting the need for comprehensive clinical assessment rather than reliance on a screening score alone. Behavioural profiling identified frequent withdrawal, social problems, attention difficulties, and a predominance of internalizing symptoms.

A more concise poster version:

Clinical suspicion should be maintained in children presenting with academic, behavioural, withdrawal, social, or internalizing symptoms, even when ISSA scores are below the diagnostic cutoff.

8. What not to claim

Avoid these statements:

“ISSA is not useful.”
“ISSA cannot diagnose ASD.”
“ISSA has low sensitivity” based on this sample alone.
“Children with low ISSA scores have more internalizing symptoms.”
“FSIQ is significantly higher than VSMS SQ.”
Any p-values or correlation claims from this dataset unless you pre-specify exploratory analyses and label them exploratory.

Your sample is small, selected, and has no comparison group. Therefore call it:

A descriptive case series / retrospective chart review of clinically diagnosed children with subtle or late-recognized ASD.

Recent evidence also indicates that diagnostic test performance varies across tools and settings, so tool scores should be integrated with clinical assessment, not treated as standalone diagnosis. See the 2025 systematic review and meta-analysis (PMID: 40274203).

9. Data points you should add, if records are available

These would substantially improve the poster:

Age at first parental concern
Age at first professional consultation
Age at formal ASD diagnosis
Diagnostic delay, calculated as age at diagnosis minus age at first concern
Why the diagnosis was delayed, coded into categories:
- adequate language or verbal ability
- average intelligence
- academic issues predominating
- anxiety/internalizing presentation
- behavioural presentation
- girl/female masking or compensatory behaviour
- misdiagnosis or prior alternate diagnosis
ASD severity/support level
Language status
Co-occurring diagnoses, such as ADHD, anxiety disorder, learning disorder, intellectual disability
Who first raised concern, parent, school, paediatrician, psychologist, etc.

The variable that will make your title defensible is actual age at diagnosis or documented diagnostic delay. Your current sheet shows children’s ages but does not yet show when ASD was diagnosed. Without that variable, say “late-recognized” only if your clinical records establish it.

Also remove names and CDGC numbers from any presentation copy. Use anonymous study IDs such as C01-C11.

i want to present behavioural profile

Present the poster specifically as a behavioural profile of clinically diagnosed children with subtle/late-recognized ASD.

Suggested title

Behavioural Profile of Children with Clinically Diagnosed Autism Spectrum Disorder and Subtle Presenting Features: A Case Series

Or shorter:

Behavioural Profile in Late-Recognized Autism Spectrum Disorder

Aim

To describe the behavioural profile of children with clinically diagnosed ASD whose presentation was subtle and whose ISSA scores were non-diagnostic or borderline.

Results to emphasize

CBCL behavioural profile

Behavioural domain	n/N	%
Withdrawn behaviour	7/11	63.6%
Social problems	7/11	63.6%
Attention problems	5/11	45.5%
Depressed features	4/11	36.4%
Anxious features	3/11	27.3%
Somatic complaints	2/11	18.2%
Aggressive behaviour	2/11	18.2%

Broad-band CBCL profile

Domain	Elevated, borderline or clinical n (%)
Internalizing problems	5 (45.5%)
Externalizing problems	4 (36.4%)

Main result sentence

The most frequent behavioural features were withdrawal and social problems, each observed in 63.6% of children, followed by attention difficulties in 45.5%. Internalizing difficulties were more frequent than externalizing difficulties, with borderline/clinical internalizing scores in 45.5% compared with borderline/clinical externalizing scores in 36.4%.

Interpretation

The behavioural profile was characterized predominantly by social withdrawal, social difficulties, attention problems, anxiety/depressive symptoms, rather than marked disruptive or aggressive behaviour. Such presentations may obscure core ASD features and contribute to late clinical recognition.

Best figure for your poster

Make one horizontal bar chart titled:

“CBCL Behavioural Profile in Children with Subtle/Late-Recognized ASD (n = 11)”

Use these bars:

Withdrawn: 63.6%
Social problems: 63.6%
Attention problems: 45.5%
Depressed: 36.4%
Anxious: 27.3%
Somatic complaints: 18.2%
Aggressive: 18.2%

Then add a smaller second chart:

“Broad-band CBCL Profile”

Elevated internalizing: 45.5%
Elevated externalizing: 36.4%

Conclusion for the poster

In this small clinical case series, children with subtle ASD features commonly showed withdrawal, social difficulties, and attention problems, with internalizing symptoms more prominent than externalizing symptoms. Behavioural and academic concerns may be the initial presentation rather than overt social-communication complaints. A non-diagnostic ISSA score should not exclude ASD when clinical developmental history and behavioural assessment indicate autism spectrum disorder.

Important wording

Use:

“clinically diagnosed ASD”
“non-diagnostic or borderline ISSA score”
“behavioural profile”
“descriptive case series”

Avoid:

“ISSA failed”
“ISSA is not diagnostic”
“ISSA is inaccurate”

More accurate wording:

“ISSA was not used as the sole basis for diagnosis; diagnosis was established clinically using developmental history, behavioural observation, and comprehensive assessment.”

The CDC clinical guidance similarly states that screening tools do not themselves establish a diagnosis and positive or concerning findings need thorough assessment.

Write a structured abstract for this study

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"Autism Spectrum Disorder"[MeSH Terms] AND diagnosis

Structured Abstract

Background:
Children with autism spectrum disorder (ASD) who have subtle clinical features may present primarily with behavioural, academic, emotional, or attention-related concerns rather than overt social-communication difficulties. This can contribute to delayed clinical recognition. Screening scores alone may not reflect the full clinical picture; ASD diagnosis requires integration of developmental history, caregiver report, behavioural observation, and clinical assessment.

Objectives:
To describe the behavioural profile of children with clinically diagnosed ASD who presented with subtle features and had non-diagnostic or borderline Indian Scale for Assessment of Autism (ISSA) scores.

Methods:
A descriptive case series was conducted among 11 children with clinically diagnosed ASD evaluated at [Centre/Department name]. Demographic data, presenting complaints, ISSA scores, Child Behavior Checklist (CBCL) behavioural profiles, Full-Scale Intelligence Quotient (FSIQ), and Vineland Social Maturity Scale (VSMS) Social Quotient were reviewed. Behavioural concerns were categorized from CBCL syndrome-scale findings and broad-band internalizing and externalizing domains. Descriptive statistics are presented as frequencies, percentages, means, standard deviations, and ranges.

Results:
The sample included 11 children, comprising 6 males (54.5%) and 5 females (45.5%), with a mean age of 11.0 years (range: 7 years 1 month to 15 years 4 months). The mean ISSA score was 67.5 ± 2.2 (range: 62-70); 10 children (90.9%) scored below the locally used cutoff of 70. Behavioural concerns were the commonest presenting complaint (54.5%), followed by academic difficulties (45.5%), social concerns (27.3%), and speech concerns (18.2%). On CBCL, withdrawal and social problems were each identified in 7 children (63.6%), while attention problems were present in 5 (45.5%). Depressive features occurred in 4 children (36.4%) and anxious symptoms in 3 (27.3%). Elevated internalizing problems were seen in 5 children (45.5%), compared with elevated externalizing problems in 4 children (36.4%). Mean FSIQ was 95.5 ± 9.5, while mean VSMS Social Quotient was 79.9 ± 10.6.

Conclusion:
Children with subtle or late-recognized ASD in this case series showed a behavioural profile marked by withdrawal, social problems, attention difficulties, and relatively prominent internalizing symptoms. Behavioural and academic complaints may obscure core ASD features. Non-diagnostic ISSA scores should not exclude ASD when a comprehensive clinical assessment supports the diagnosis.

is it good poster topic

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"Autism Spectrum Disorder"[MeSH Terms] AND diagnosis

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CDC autism diagnostic assessment screening tools not diagnosis clinical diagnosis

Yes. It is a good, clinically relevant poster topic, especially for psychiatry, developmental paediatrics, psychology, or behavioural-science meetings.

Why it works

It has a focused clinical question: what behavioural features are seen in children with subtle or late-recognized ASD?
Your data have a clear pattern: withdrawal and social problems were each present in 63.6%, attention difficulties in 45.5%, and internalizing difficulties were somewhat more prominent than externalizing symptoms.
It has a practical message: children may enter services for academic, behavioural, anxiety, or withdrawal concerns rather than an obvious ASD complaint.
It reinforces appropriate clinical practice: no single screening or assessment score should determine ASD diagnosis in isolation, consistent with CDC diagnostic guidance.

The main limitation

Your current sample is small, n = 11, so present it as a:

Descriptive case series
or
Retrospective descriptive chart review

Do not make it a study “proving ISSA is non-diagnostic” or “testing ISSA accuracy.” Your data do not include a non-ASD comparison group, sensitivity, specificity, or a formal diagnostic-validation design.

One important correction

Use “late-recognized” or “late-diagnosed” only if you have recorded:

age at diagnosis, and ideally
age at first parental/professional concern.

If you only have the children’s current ages, a safer title is:

Behavioural Profile of Children with Clinically Diagnosed Autism Spectrum Disorder and Subtle Presenting Features: A Descriptive Case Series

If you can document delayed diagnosis, use:

Behavioural Profile of Children with Late-Recognized Autism Spectrum Disorder and Subtle Presenting Features: A Case Series

Best central message

In children with subtle ASD presentations, withdrawal, social difficulties, attention problems, and internalizing symptoms may be more prominent than disruptive behaviour. These children may initially present with academic or behavioural concerns, delaying ASD recognition.

That is a credible, useful, and poster-worthy topic. Recent diagnostic evidence also supports cautious interpretation of individual autism assessment tools rather than treating a single score as definitive, as summarized in this 2025 systematic review (PMID: 40274203).

Make graphs from my Excel data

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mkdir -p "/tmp/workspace/asd_behavioural_poster_graphs/output" 66 python - <<'PY' import pandas as pd from pathlib import Path p=Path('/tmp/workspace/attachments/bc8e6272-90da-4017-92b5-e411ef937585/POSTER PRESENTATION.xlsx') df=pd.read_excel(p) print(df.shape) print(df[['PRESENTING COMPLAINTS','CBCL','INTERNALIZING','EXTERNALIZING']].to_string(index=False)) PY

Running Command

python - <<'PY' import pandas as pd from pathlib import Path p=Path('/tmp/workspace/attachments/bc8e6272-90da-4017-92b5-e411ef937585/POSTER PRESENTATION.xlsx') df=pd.read_excel(p) print('shape=', df.shape) print(df[['PRESENTING COMPLAINTS','CBCL','INTERNALIZING','EXTERNALIZING']].to_string(index=False)) PY

Running Command

python - <<'PY' from pathlib import Path import pandas as pd import matplotlib.pyplot as plt from matplotlib.ticker import PercentFormatter from openpyxl import Workbook from openpyxl.styles import Font, PatternFill, Alignment, Border, Side from openpyxl.chart import BarChart, Reference from openpyxl.chart.label import DataLabelList from openpyxl.drawing.image import Image as XLImage source=Path('/tmp/workspace/attachments/bc8e6272-90da-4017-92b5-e411ef937585/POSTER PRESENTATION.xlsx') out=Path('/tmp/workspace/asd_behavioural_poster_graphs/output') out.mkdir(parents=True, exist_ok=True) df=pd.read_excel(source) n=len(df) # counts based on keyword occurrences in semicolon-like multi-response text cbcl_specs=[ ('Withdrawn', ['WITHDRAWN']), ('Social problems', ['SOCIAL']), ('Attention problems', ['ATTENTION']), ('Depressive features', ['DEPRESSED']), ('Anxious features', ['ANXIOUS']), ('Somatic complaints', ['SOMATIC']), ('Aggressive behaviour', ['AGGRESSIVE','AGGRESSION']), ] cbcl=[] for label, keys in cbcl_specs: count=df['CBCL'].fillna('').str.upper().apply(lambda x:any(k in x for k in keys)).sum() cbcl.append((label,int(count),count/n*100)) complaint_specs=[ ('Behavioural concern', ['BEHAV']), ('Academic difficulty', ['ACADEMIC']), ('Social concern', ['SOCIAL']), ('Speech/language concern', ['SPEECH']), ] complaints=[] for label, keys in complaint_specs: count=df['PRESENTING COMPLAINTS'].fillna('').str.upper().apply(lambda x:any(k in x for k in keys)).sum() complaints.append((label,int(count),count/n*100)) # Styling plt.rcParams.update({'font.family':'DejaVu Sans','font.size':13, 'axes.titleweight':'bold'}) navy='#12355B'; teal='#1B998B'; gold='#F4A261'; coral='#E76F51'; pale='#E9F1F7'; grey='#64748B' def horizontal_chart(items,title,filename,color,footnote=None): labels=[x[0] for x in items][::-1]; vals=[x[2] for x in items][::-1]; counts=[x[1] for x in items][::-1] fig,ax=plt.subplots(figsize=(11,6.5), dpi=220) fig.patch.set_facecolor('white'); ax.set_facecolor('white') bars=ax.barh(labels, vals, color=color, height=.62) ax.set_xlim(0,80); ax.set_xlabel('Children (%)'); ax.xaxis.set_major_formatter(PercentFormatter(100)) ax.set_title(title,loc='left',pad=18,color=navy,fontsize=18) ax.grid(axis='x',alpha=.20); ax.set_axisbelow(True) ax.spines[['top','right','left']].set_visible(False); ax.spines['bottom'].set_color('#CBD5E1') ax.tick_params(axis='y',length=0); ax.tick_params(axis='x',colors=grey) for bar,v,c in zip(bars,vals,counts): ax.text(v+1,bar.get_y()+bar.get_height()/2,f'{c}/{n} ({v:.1f}%)',va='center',fontsize=12,color='#1F2937',fontweight='bold') if footnote: fig.text(.125,.02,footnote,fontsize=10,color=grey) plt.tight_layout(rect=[0, .05 if footnote else 0, 1, 1]) fig.savefig(out/filename,bbox_inches='tight',facecolor='white') plt.close(fig) horizontal_chart(cbcl,'CBCL Behavioural Profile in Children with Subtle ASD Features (n = 11)','Figure_1_CBCL_behavioural_profile.png',teal) horizontal_chart(complaints,'Presenting Complaints at Assessment (n = 11)','Figure_2_presenting_complaints.png',gold,'Categories are not mutually exclusive; a child may have more than one presenting complaint.') # stacked 100% broad-band profile profiles=[] for domain in ['INTERNALIZING','EXTERNALIZING']: vc=df[domain].value_counts() profiles.append([vc.get('NORMAL',0)/n*100,vc.get('BORDERLINE',0)/n*100,vc.get('CLINICAL',0)/n*100]) fig,ax=plt.subplots(figsize=(10,6.5),dpi=220) fig.patch.set_facecolor('white'); ax.set_facecolor('white') labels=['Internalizing\nproblems','Externalizing\nproblems']; categories=['Normal','Borderline','Clinical']; colors=['#B8E0D2',gold,coral] base=[0,0] for ix,(cat,color) in enumerate(zip(categories,colors)): vals=[p[ix] for p in profiles] bars=ax.bar(labels,vals,bottom=base,color=color,width=.55,label=cat,edgecolor='white',linewidth=1.5) for b,v in zip(bars,vals): if v>=15: ax.text(b.get_x()+b.get_width()/2,b.get_y()+v/2,f'{v:.1f}%',ha='center',va='center',fontsize=12,fontweight='bold',color='#1F2937') base=[a+b for a,b in zip(base,vals)] ax.set_ylim(0,100); ax.set_ylabel('Children (%)'); ax.yaxis.set_major_formatter(PercentFormatter(100)); ax.set_title('CBCL Broad-band Behavioural Profile (n = 11)',loc='left',pad=18,color=navy,fontsize=18) ax.grid(axis='y',alpha=.20); ax.set_axisbelow(True); ax.spines[['top','right']].set_visible(False); ax.spines['left'].set_color('#CBD5E1'); ax.spines['bottom'].set_color('#CBD5E1') ax.legend(ncol=3,loc='upper center',bbox_to_anchor=(.5,-.12),frameon=False) plt.tight_layout() fig.savefig(out/'Figure_3_CBCL_broadband_profile.png',bbox_inches='tight',facecolor='white'); plt.close(fig) # Excel summary workbook wb=Workbook(); ws=wb.active; ws.title='Graph Data' ws.append(['Poster graph data',None,None]); ws.append(['Total sample (n)',n,None]); ws.append([]) ws.append(['Figure 1. CBCL behavioural profile','Count','Percent']) for a,b,c in cbcl: ws.append([a,b,c/100]) ws.append([]); ws.append(['Figure 2. Presenting complaints','Count','Percent']) for a,b,c in complaints: ws.append([a,b,c/100]) ws.append([]); ws.append(['Figure 3. CBCL broad-band profile','Normal','Borderline','Clinical']) for lab,vals in zip(['Internalizing problems','Externalizing problems'],profiles): ws.append([lab]+[x/100 for x in vals]) for cell in ws[1]: cell.font=Font(bold=True,size=14,color='FFFFFF'); cell.fill=PatternFill('solid',fgColor='12355B') for r in [4,13,20]: for cell in ws[r]: cell.font=Font(bold=True,color='FFFFFF'); cell.fill=PatternFill('solid',fgColor='1B998B'); cell.alignment=Alignment(horizontal='center') for row in ws.iter_rows(): for cell in row: cell.alignment=Alignment(vertical='center') if isinstance(cell.value,float): cell.number_format='0.0%' ws.column_dimensions['A'].width=37; ws.column_dimensions['B'].width=15; ws.column_dimensions['C'].width=15; ws.column_dimensions['D'].width=15 thin=Side(style='thin',color='D1D5DB') for row in ws.iter_rows(min_row=4,max_row=22,min_col=1,max_col=4): for cell in row: cell.border=Border(bottom=thin) # Insert pngs into separate sheet viz=wb.create_sheet('Poster Graphs') viz['A1']='Poster-ready figures'; viz['A1'].font=Font(bold=True,size=16,color='12355B') for pos,imgfile in [('A3','Figure_1_CBCL_behavioural_profile.png'),('A31','Figure_2_presenting_complaints.png'),('A58','Figure_3_CBCL_broadband_profile.png')]: im=XLImage(str(out/imgfile)); im.width=880; im.height=520; viz.add_image(im,pos) wb.save(out/'ASD_behavioural_profile_graphs.xlsx') # create PDF-like source note (out/'README.txt').write_text('Files created from POSTER PRESENTATION.xlsx (n=11).\n\nFigure 1: CBCL behavioural profile; CBCL syndrome terms were counted when present in the recorded CBCL text.\nFigure 2: Presenting complaints; categories are multiple response.\nFigure 3: CBCL broad-band profile from the recorded Internalizing and Externalizing categories.\n\nUse only de-identified data in posters.\n',encoding='utf-8') print('Created:') for p in sorted(out.iterdir()): print(p.name, p.stat().st_size) PY

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python - <<'PY' from pathlib import Path from openpyxl import load_workbook out=Path('/tmp/workspace/asd_behavioural_poster_graphs/output') wb=load_workbook(out/'ASD_behavioural_profile_graphs.xlsx') print(wb.sheetnames) for f in ['Figure_1_CBCL_behavioural_profile.png','Figure_2_presenting_complaints.png','Figure_3_CBCL_broadband_profile.png']: p=out/f print(f, 'exists=',p.exists(), 'bytes=',p.stat().st_size) print('embedded images=',len(wb['Poster Graphs']._images)) PY

Created three poster-ready graphs from your Excel data:

CBCL Behavioural Profile
- Withdrawal, social problems, attention problems, anxiety, depressive features, somatic complaints, aggression.
Presenting Complaints
- Behavioural, academic, social, and speech/language concerns.
- Clearly labelled as multiple-response data.
CBCL Broad-band Profile
- Normal, borderline, and clinical internalizing versus externalizing domains.

Download individual high-resolution PNG files:

Figure 1 CBCL behavioural profile

Image · PNG

Figure 2 presenting complaints

Image · PNG

Figure 3 CBCL broadband profile

Image · PNG

I also prepared an Excel file containing the graph data and all three figures embedded on a separate worksheet:

ASD behavioural profile graphs

Spreadsheet · XLSX

my idea for this poster was children with apparently good speech (no concerns in speech), apparently fair social interaction may have ASD now create abstract

Reading File

Searching PubMed

"Autism Spectrum Disorder"[MeSH Terms] AND diagnosis

Searching the Web

CDC autism diagnosis children no single tool clinical observation developmental history

Proposed Title

Beyond Speech Delay: Behavioural Presentation of Children with Clinically Diagnosed Autism Spectrum Disorder and No Primary Speech Complaint

This title is stronger and safer than saying “fair social interaction,” because your sheet records presenting complaints, not an objective measure of social interaction. You can state that social concerns were not the primary referral complaint for most children.

Structured Abstract

Background:
Autism spectrum disorder (ASD) may be overlooked in children who have functional spoken language and do not present primarily with speech delay or overt social concerns. Instead, behavioural, academic, emotional, or attention-related difficulties may be the reason for referral. Such presentations can obscure underlying social-communication differences and delay clinical recognition.

Objectives:
To describe the presenting complaints and behavioural profile of children with clinically diagnosed ASD in whom speech and social concerns were not prominent at referral.

Methods:
A descriptive case series was conducted among 11 children with clinically diagnosed ASD assessed at [centre/department]. Demographic characteristics, presenting complaints, Indian Scale for Assessment of Autism (ISSA) scores, Child Behavior Checklist (CBCL) findings, Full-Scale Intelligence Quotient (FSIQ), and Vineland Social Maturity Scale (VSMS) Social Quotient were reviewed. CBCL syndrome-scale findings and broad-band internalizing and externalizing domains were summarized using frequencies, percentages, means, standard deviations, and ranges.

Results:
The sample comprised 11 children, including 6 males (54.5%) and 5 females (45.5%), with a mean age of 11.0 years (range: 7 years 1 month to 15 years 4 months). Speech was a presenting concern in only 2 children (18.2%), while social concerns were documented as a presenting complaint in 3 children (27.3%). Behavioural concerns were the commonest presenting complaint, occurring in 6 children (54.5%), followed by academic difficulties in 5 children (45.5%). The mean ISSA score was 67.5 ± 2.2 (range: 62-70). On CBCL, withdrawal and social problems were each identified in 7 children (63.6%), attention problems in 5 (45.5%), depressive features in 4 (36.4%), and anxious features in 3 (27.3%). Borderline or clinical internalizing problems were present in 5 children (45.5%), whereas borderline or clinical externalizing problems were present in 4 children (36.4%). Mean FSIQ was 95.5 ± 9.5 and mean VSMS Social Quotient was 79.9 ± 10.6.

Conclusion:
In this small case series, most children with clinically diagnosed ASD did not present with speech or social concerns as the primary referral complaint. Behavioural and academic difficulties, together with withdrawal, social problems, attention difficulties, and internalizing symptoms, formed the prominent clinical profile. Clinicians should consider ASD assessment in children with such presentations even when spoken language appears adequate and overt social concerns are not initially reported. Diagnosis should rest on comprehensive developmental and clinical assessment rather than any single assessment score, consistent with CDC diagnostic guidance.

Keywords: Autism spectrum disorder; behavioural profile; spoken language; social communication; internalizing symptoms; delayed recognition; CBCL.

what are the results if we remove social and speech dalay presentation patients

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Running Command

python - <<'PY' import pandas as pd, re p='/tmp/workspace/attachments/bc8e6272-90da-4017-92b5-e411ef937585/POSTER PRESENTATION.xlsx' df=pd.read_excel(p) mask=~df['PRESENTING COMPLAINTS'].str.upper().str.contains('SOCIAL|SPEECH', regex=True, na=False) x=df[mask].copy() print('Included original SR:',x['SR. NO'].tolist(), 'n=',len(x)) print(x[['SR. NO','PRESENTING COMPLAINTS','AGE','SEX','ISSA','CBCL','INTERNALIZING','EXTERNALIZING','FSIQ','VSMS SQ']].to_string(index=False)) def age_m(s): s=str(s).upper(); yy=re.search(r'(\d+)\s*YEAR',s); mm=re.search(r'(\d+)\s*M',s) return (int(yy.group(1))*12 if yy else 0)+(int(mm.group(1)) if mm else 0) x['age_m']=x.AGE.map(age_m) print('\nsex',x.SEX.value_counts().to_dict()) for c in ['age_m','ISSA','FSIQ','VSMS SQ']: print(c, 'mean',round(x[c].mean(),1),'sd',round(x[c].std(),1),'median',round(x[c].median(),1),'range',f'{x[c].min()}-{x[c].max()}') for col in ['INTERNALIZING','EXTERNALIZING']: vc=x[col].value_counts(); print(col,vc.to_dict(),'elevated',int(vc.get('BORDERLINE',0)+vc.get('CLINICAL',0)),round((vc.get('BORDERLINE',0)+vc.get('CLINICAL',0))/len(x)*100,1)) for label,terms in [('Withdrawn',['WITHDRAWN']),('Social problems',['SOCIAL']),('Attention problems',['ATTENTION']),('Depressive features',['DEPRESSED']),('Anxious features',['ANXIOUS']),('Somatic complaints',['SOMATIC']),('Aggressive behaviour',['AGGRESSIVE','AGGRESSION'])]: n=x.CBCL.str.upper().apply(lambda s:any(k in s for k in terms)).sum(); print(label,n,round(n/len(x)*100,1)) for label,term in [('Behavioural concern','BEHAV'),('Academic difficulty','ACADEMIC')]: n=x['PRESENTING COMPLAINTS'].str.upper().str.contains(term).sum();print(label,n,round(n/len(x)*100,1)) print('ISSA below70', (x.ISSA<70).sum(),round((x.ISSA<70).mean()*100,1)) PY

Searching the Web

CDC autism spectrum disorder diagnosis children developmental history observation social communication speech language

If you exclude every child whose presenting complaint included “social” or “speech”, your study subgroup becomes 6 children.

Included children: those presenting with academic and/or behavioural concerns only.
Excluded children: 5 children with social and/or speech listed as a presenting complaint, including mixed complaints such as “academic + social.”

Results in the restricted subgroup, n = 6

Demographic and assessment profile

Variable	Result
Female	4/6 (66.7%)
Male	2/6 (33.3%)
Mean age	10.9 years
Age range	7 years 6 months to 15 years 4 months
Mean ISSA score	68.3 ± 1.0
ISSA range	67-70
ISSA <70	5/6 (83.3%)
Mean FSIQ	96.0 ± 10.4
Mean VSMS Social Quotient	80.7 ± 10.1

Presenting complaints

Complaint	n (%)
Behavioural concern	5 (83.3%)
Academic difficulty	3 (50.0%)
Social concern	0 (0%) by selection
Speech concern	0 (0%) by selection

CBCL behavioural profile

CBCL feature	n (%)
Social problems	3 (50.0%)
Anxious features	3 (50.0%)
Depressive features	3 (50.0%)
Aggressive behaviour	3 (50.0%)
Withdrawal	2 (33.3%)
Attention problems	2 (33.3%)
Somatic complaints	2 (33.3%)

Broad-band CBCL profile

Domain	Result
Elevated internalizing problems, borderline or clinical	4/6 (66.7%)
Elevated externalizing problems, borderline or clinical	3/6 (50.0%)
Clinical internalizing problems	3/6 (50.0%)
Clinical externalizing problems	1/6 (16.7%)

Stronger poster result statement

Of 11 clinically diagnosed children with ASD, 6 children (54.5%) had no speech or social concern documented as a presenting complaint and were referred primarily for behavioural and/or academic difficulties. Behavioural concerns were present in 83.3% and academic difficulties in 50.0%. Despite the absence of speech or social concerns at referral, CBCL profiling identified social problems in 50.0% of children. Internalizing difficulties were prominent, with elevated internalizing scores in 66.7%, compared with elevated externalizing scores in 50.0%. Mean FSIQ was 96.0 ± 10.4, suggesting broadly average cognitive ability in this subgroup.

Better conclusion for your actual data

Children with clinically diagnosed ASD may present to services primarily with behavioural or academic concerns, without a documented speech or social complaint. In this subgroup, clinically relevant internalizing symptoms and CBCL social problems were frequent, indicating that apparently adequate speech or superficially fair social interaction should not preclude a comprehensive ASD assessment.

Important wording change

Do not write “children with no speech delay” because your Excel sheet documents only presenting complaints, not a formal speech-language assessment or developmental language history.

Use this instead:

“Children without speech or social concerns as the presenting complaint.”

That wording is accurate and defensible. ASD still requires evidence of social-communication differences across development and contexts, even if families or referrers did not initially identify them as the primary problem, as outlined in CDC diagnostic criteria guidance.

Rewrite the abstract using the restricted subgroup (n=6)

Reading File

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