1.1 Age and Gender Distribution Table 1.1: Age and Gender

1.1 Age and Gender Distribution Table 1.1: Age and Gender — Both Groups PARAMETER GROUP A — Test (n=34) GROUP B — Control (n=36) p-value Age (years) — Mean ± SD 41.94 ± 13.21 42.81 ± 13.16 p > 0.05 (NS) Age Range (years) 18 – 68 18 – 69 — Gender Female — n (%) 22 (64.7%) 23 (63.9%) p > 0.05 (NS) Male — n (%) 12 (35.3%) 13 (36.1%) p > 0.05 (NS) Female : Male Ratio 1.83 : 1 1.77 : 1 — NS = Not Significant. Both groups are statistically comparable for age and gender. Independent t-test for age; Chi-Square for gender. The mean age of participants in Group A was 41.94 ± 13.21 years (range 18–65 years) and in Group B was 42.81 ± 13.16 years (range 18–65 years). The two groups were statistically comparable for age (independent t-test, p > 0.05), confirming effective randomization. This age distribution is consistent with the peak prevalence of dentinal hypersensitivity reported in the third through sixth decades of life, as documented by Addy M (Int Dent J, 2002) and the systematic review of Favaro Zeola L et al. (J Dent, 2019) — which reported the highest DH prevalence between 20 and 50 years.(is it required here or in discussion) Regarding gender distribution, females were the predominant gender in both groups: Group A comprised 22 females (64.7%) and 12 males (35.3%); Group B comprised 23 females (63.9%) and 13 males (36.1%). The female-to-male ratio was approximately 1.8:1 in both groups, with no statistically significant inter-group difference (Chi-Square, p > 0.05). The higher representation of female participants is consistent with published prevalence data indicating greater healthcare-seeking behaviour for dental conditions among women, as well as the greater prevalence of DH in females noted by Rees JS (IJDR, 2000) and Gillam DG et al. (Community Dent Health, 1997). .(is it required here or in discussion) graph Figure 1: Age Distribution Group A vs Group B. Both groups are comparable for age (mean ~42 years) and gender (female predominance ~64%).please frame write up in results n observations for thesis

Here is a clean, thesis-ready Results and Observations write-up for Section 1.1:

1.1 Age and Gender Distribution

Table 1.1 presents the demographic profile of both study groups with respect to age and gender.

The mean age of participants in Group A (Test) was 41.94 ± 13.21 years (range: 18–68 years), and in Group B (Control) was 42.81 ± 13.16 years (range: 18–69 years). No statistically significant difference was observed between the two groups for age (independent t-test, p > 0.05), confirming effective baseline randomization and demographic comparability.

Regarding gender distribution, females were predominant in both groups. Group A comprised 22 females (64.7%) and 12 males (35.3%), while Group B comprised 23 females (63.9%) and 13 males (36.1%). The female-to-male ratio was approximately 1.83:1 in Group A and 1.77:1 in Group B. Inter-group comparison revealed no statistically significant difference in gender distribution (Chi-Square test, p > 0.05).

Figure 1 illustrates the age distribution across both groups, confirming comparable profiles with a mean age of approximately 42 years and female predominance of approximately 64% in each group.

Overall, both groups were well-matched for age and gender, ensuring that any observed differences in outcomes could be attributed to the intervention rather than demographic confounders.

Regarding Your Two Parenthetical Queries

"(Is it required here or in discussion?)" — answered separately for each point:

1. Age distribution and DH peak prevalence (Addy 2002, Favaro Zeola 2019): Keep a brief mention in Results — one sentence stating that the observed age range corresponds to the known peak prevalence of dentinal hypersensitivity. The fuller elaboration with multiple citations belongs in the Discussion, where you contextualise your findings against the literature.

Suggested sentence for Results: "The observed age distribution, predominantly within the third to sixth decades of life, is consistent with the reported peak prevalence of dentinal hypersensitivity in this age group."

2. Female predominance and healthcare-seeking behaviour (Rees 2000, Gillam 1997): This interpretive point — explaining why females are over-represented — belongs entirely in the Discussion, not in Results. The Results section should only report what was observed (the ratio and p-value), without inferring reasons. Move the citations by Rees JS and Gillam DG to your Discussion under the heading of gender distribution and DH prevalence.

Summary of Placement

Content	Results	Discussion
Mean age, SD, range, p-value	✅	—
Gender counts, percentages, ratio, p-value	✅	—
Age corresponds to DH peak prevalence (brief)	✅ (one line)	✅ (elaborate)
Female predominance / healthcare-seeking behaviour	❌	✅
Citations (Addy, Favaro Zeola, Rees, Gillam)	❌	✅

Table 1.2: Socio-Demographic Profile — Both Groups PARAMETER GROUP A (n=34) GROUP B (n=36) Test p Marital Status Married 27 (79.4%) 30 (83.3%) Fisher's Exact NS Unmarried 7 (20.6%) 6 (16.7%) Education Level Primary (up to Class 7) 6 (17.6%) 8 (22.2%) Chi-Square NS Secondary (Class 8–10) 14 (41.2%) 15 (41.7%) Higher Secondary (Class 11–12) 5 (14.7%) 7 (19.4%) Graduate and Above 9 (26.5%) 6 (16.7%) Occupation Homemaker 20 (58.8%) 21 (58.3%) Chi-Square NS Service 7 (20.6%) 7 (19.4%) Labourer 3 (8.8%) 1 (2.8%) Business 2 (5.9%) 2 (5.6%) Farmer 2 (5.9%) 5 (13.9%) Socio-Economic Status Lower Middle 13 (38.2%) 15 (41.7%) Chi-Square NS Middle 17 (50.0%) 15 (41.7%) Upper Middle 4 (11.8%) 6 (16.7%) NS = Not Significant (p > 0.05). Chi-Square or Fisher's Exact test as appropriate. SES = Socio-Economic Status assessed by modified Kuppuswamy scale. The majority of participants in both groups were married: Group A — 27 (79.4%); Group B — 30 (83.3%). Secondary education (Class 9–12) was the most common educational level: Group A — 14 (41.2%); Group B — 15 (41.7%). The occupation most frequently observed in both groups was homemaker: Group A — 20 (58.8%); Group B — 21 (58.3%), reflecting the largely semi-urban patient population attending the outpatient department. Service-sector employment was the second most common occupation in both groups (Group A: 7, 20.6%; Group B: 7, 19.4%). Regarding socio-economic status (SES) assessed by the modified Kuppuswamy scale, Middle SES was the dominant category in Group A (17 participants, 50.0%) while Lower Middle SES was dominant in Group B (15 participants, 41.7%). Upper Middle SES accounted for 11.8% of Group A and 16.7% of Group B. No participants from the Upper SES category were enrolled, consistent with the socio-economic profile of the outpatient department catchment area. No statistically significant difference was observed between groups for marital status, education, occupation, or SES (all p > 0.05). Figure 2: Key Demographic Parameters — Marital Status, Socio-Economic Status, Education Level, and Occupation. Both groups show comparable distributions across all parameters (p > 0.05 for all comparisons).write results n observations

1.2 Socio-Demographic Profile

Table 1.2 summarises the socio-demographic characteristics of both study groups across four parameters: marital status, education level, occupation, and socio-economic status (SES).

Marital Status: The majority of participants in both groups were married — 27 (79.4%) in Group A and 30 (83.3%) in Group B. Unmarried participants accounted for 7 (20.6%) in Group A and 6 (16.7%) in Group B. No statistically significant difference was observed between the groups (Fisher's Exact test, p > 0.05).

Education Level: Secondary education (Class 8–10) was the most frequently reported educational level in both groups — 14 participants (41.2%) in Group A and 15 participants (41.7%) in Group B. Graduate and above was the next predominant category in Group A (9, 26.5%), while primary education (up to Class 7) was more common in Group B (8, 22.2%). Higher secondary education (Class 11–12) accounted for 5 (14.7%) in Group A and 7 (19.4%) in Group B. The inter-group difference in educational distribution was not statistically significant (Chi-Square test, p > 0.05).

Occupation: Homemaker was the most prevalent occupational category in both groups — 20 participants (58.8%) in Group A and 21 participants (58.3%) in Group B, reflecting the semi-urban outpatient population of the study centre. Service-sector employment was the second most common category in both groups (Group A: 7, 20.6%; Group B: 7, 19.4%). Labourer, business, and farmer categories were represented in smaller proportions across both groups, with no substantial inter-group disparity. No statistically significant difference was observed in occupational distribution between the two groups (Chi-Square test, p > 0.05).

Socio-Economic Status: SES was assessed using the modified Kuppuswamy scale. In Group A, the Middle SES category was predominant (17 participants, 50.0%), followed by Lower Middle (13, 38.2%) and Upper Middle (4, 11.8%). In Group B, Lower Middle SES was the most common category (15 participants, 41.7%), followed by Middle (15, 41.7%) and Upper Middle (6, 16.7%). No participants from the Upper SES category were enrolled in either group, consistent with the socio-economic profile of the outpatient department catchment area. The inter-group difference in SES distribution was not statistically significant (Chi-Square test, p > 0.05).

Figure 2 depicts the distribution of all four demographic parameters across both groups, confirming broadly comparable profiles.

Overall, both groups were well-matched across all socio-demographic variables. The absence of statistically significant differences (p > 0.05 for all comparisons) confirms that the two groups were homogeneous at baseline, thereby minimising the influence of socio-demographic confounders on study outcomes.

Note on Discussion Placement

The observation regarding homemaker predominance reflecting a semi-urban OPD catchment population is an interpretive remark. A brief mention is acceptable in Results as contextual explanation, but any elaboration on how SES or occupation may influence oral hygiene practices, dental awareness, or treatment-seeking behaviour should be reserved for the Discussion section.

please prepare more colourful n editable figure

Writing File

Generating Image

Figure 2: Key Demographic Characteristics — Group A vs Group B

Here's what's been prepared for you:

Colorful Figure (above)

Vivid red (Group A) and steel blue (Group B) with percentage labels, clean white panels on a light background — much more visually engaging than the original monochrome version.

Fully Editable Python Script — `figure2_demographics.py`

The script is saved and ready to run. Key things you can edit at the top of the file:

Variable	What it controls
`COLOR_A = "#E63946"`	Group A bar colour — change hex code to anything
`COLOR_B = "#457B9D"`	Group B bar colour
`FIGURE_BG = "#F8F9FA"`	Overall background colour
`marital_A`, `ses_A`, etc.	Data values — update if numbers change
`BAR_WIDTH = 0.35`	Width of each bar
`DPI = 150`	Output resolution

To Run

pip install matplotlib numpy
python figure2_demographics.py

Two Output Formats

figure2_demographics.png — high-resolution raster image for Word/PowerPoint
figure2_demographics.svg — fully editable vector file; open in Inkscape (free) or Adobe Illustrator to move, recolour, or relabel any element individually

prepare it also clear image

Writing File

Generating Image

Figure 1: Age Distribution and Gender Distribution — Both Groups

Both the figure preview and the fully editable Python script (figure1_age_gender.py) are ready. Here's a quick guide:

What the figure contains

Panel	Content
(A) Histogram	Overlapping age bars — red (Group A) + blue (Group B) with dashed mean lines
(B) Donut — Group A	Red/pink split: Female 22 (64.7%) / Male 12 (35.3%)
(C) Donut — Group B	Green/mint split: Female 23 (63.9%) / Male 13 (36.1%)

Key things to edit in the script

Variable	What it changes
`ages_A`, `ages_B`	Individual age values — replace with your actual raw data
`mean_A`, `sd_A` etc.	Stats shown in legend
`COLOR_A_HIST`, `COLOR_B_HIST`	Histogram bar colours
`DONUT_A`, `DONUT_B`	`[Female colour, Male colour]` for each donut
`BINS`	Age bin edges for the histogram
`DPI = 180`	Output resolution (use 300 for print-quality)

To run

pip install matplotlib numpy
python figure1_age_gender.py

Saves both PNG (for Word/PowerPoint) and SVG (fully editable in Inkscape or Adobe Illustrator — every bar, label, and colour is independently movable).

above 65age is exclusion according to this manage the image it showing 68 to 78 range show it up to 65 only

Reading File

Editing File

All four changes are done. Here's a summary of every fix made:

Changes Made to `figure1_age_gender.py`

What was changed	Before	After
Ages in data	Included 67, 68, 69	Capped at 65 (exclusion criteria respected)
Histogram bins	`[18, 28, 38, 48, 58, 68, 78]`	`[18, 28, 38, 48, 58, 66]`
X-axis range	`set_xlim(15, 80)`	`set_xlim(15, 68)` — ends cleanly after 65
X-axis tick labels	Went up to 70–80	Now shows `18, 28, 38, 48, 58, 65`
Subtitle	No mention of age range	Now reads: "Age range: 18–65 years"

To regenerate the figure

python figure1_age_gender.py

Note on the image preview: The generate_image tool has reached its monthly plan limit. To get the updated rendered preview directly here, please upgrade your subscription or contact support. The Python script itself is fully corrected and will produce the right output when you run it locally.

Prakriti assessment was conducted using the standard Prakriti Pariksha methodology documented in Ashtanga Hridayam and the standardised Prakriti assessment tool. The distribution of dominant Prakriti types is presented in Table 1.3 and Figure 3. Vata-Pitta and Pitta-Kapha were the two dominant dual-Dosha Prakriti types in both groups. In Group A, Vata-Pitta Prakriti accounted for 10 participants (29.4%) and Pitta-Kapha for 10 participants (29.4%) — together comprising 58.8% of Group A. In Group B, Vata-Pitta and Pitta-Kapha were equally distributed at 11 participants each (30.6% each) — together comprising 61.2% of Group B. Vata-Kapha Prakriti accounted for 20.6% of Group A and 11.1% of Group B. Pure Vata, pure Pitta, and pure Kapha Prakriti types were less commonly represented in both groups. Sama Prakriti (balanced tri-dosha constitution) was observed in 2 participants (5.6%) in Group B and none in Group A. The predominance of dual-Dosha Prakriti types — particularly those involving Vata (Vata-Pitta and Vata-Kapha combined = 50.0% Group A; 41.7% Group B) — is clinically significant in the Ayurvedic framework. Dantaharsha is classified as a Vataja or Vata-Pittaja Danta Roga in classical texts (Sushruta Samhita NS 16/3; A.H. U. 22/13), and the predominance of Vata-dominant Prakriti types in the enrolled population supports the classical aetiological understanding of the condition. Both groups showed comparable Prakriti distributions (Chi-Square, p > 0.05). Table 1.4: Prakriti Distribution — Both Groups PRAKRITI TYPE GROUP A — n (%) GROUP B — n (%) Dominant Dosha p-value Vata-Pitta 10 (29.4%) 11 (30.6%) Vata + Pitta NS Pitta-Kapha 10 (29.4%) 11 (30.6%) Pitta + Kapha NS Vata-Kapha 7 (20.6%) 4 (11.1%) Vata + Kapha NS Pitta (Pure) 4 (11.8%) 1 (2.8%) Pitta NS Vata (Pure) 2 (5.9%) 4 (11.1%) Vata NS Kapha (Pure) 1 (2.9%) 3 (8.3%) Kapha NS Sama Prakriti (Balanced) 0 (0.0%) 2 (5.6%) Tri-Dosha balanced NS NS = Not Significant. Chi-Square test. Groups are comparable for Prakriti distribution (p > 0.05 for all comparisons). Vata-containing Prakriti = Vata-Pitta + Vata-Kapha + Vata (pure).short write up

1.3 Prakriti Distribution

Prakriti assessment was conducted using the standard Prakriti Pariksha methodology as documented in Ashtanga Hridayam, employing a standardised assessment tool. The distribution of dominant Prakriti types across both groups is presented in Table 1.3 and Figure 3.

Dual-Dosha Prakriti types were predominant in both groups. Vata-Pitta and Pitta-Kapha were the most frequently observed constitutions — each accounting for 10 participants (29.4%) in Group A and 11 participants (30.6%) in Group B, together comprising 58.8% of Group A and 61.2% of Group B. Vata-Kapha Prakriti was present in 7 participants (20.6%) in Group A and 4 participants (11.1%) in Group B.

Among pure single-Dosha types, Pitta Prakriti was observed in 4 participants (11.8%) in Group A and 1 participant (2.8%) in Group B; Vata Prakriti in 2 (5.9%) and 4 (11.1%); and Kapha Prakriti in 1 (2.9%) and 3 (8.3%) respectively. Sama Prakriti (balanced tri-Dosha constitution) was observed in 2 participants (5.6%) in Group B and none in Group A.

Collectively, Vata-containing Prakriti types (Vata-Pitta + Vata-Kapha + pure Vata) accounted for 50.0% of Group A and 41.7% of Group B. No statistically significant inter-group difference was observed in Prakriti distribution (Chi-Square test, p > 0.05), confirming baseline comparability.

Note on Discussion Placement

The paragraph linking Vata-dominant Prakriti predominance to the classical Ayurvedic aetiology of Dantaharsha (Vataja / Vata-Pittaja Danta Roga — Sushruta Samhita NS 16/3; A.H. U. 22/13) is interpretive and belongs in the Discussion. In the Results section, retain only the observation that Vata-containing types were the most prevalent; reserve the clinical and textual significance for Discussion.

Suggested closing line for Results: "The predominance of Vata-containing Prakriti types in both groups is noteworthy in the context of the Ayurvedic understanding of Dantaharsha and will be discussed further."

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