Can you tell me the topics which have come IN PSM chapter wise in tese papers I send you

It looks like you mentioned sending papers, but I don't see any attachments in your message. Could you please share the files?
Asking for Preferences

Please upload the PSM question papers you'd like me to analyze.

Reading File
Running Command

cd /tmp/workspace/attachments/b0ed0c88-fa80-49e2-b122-1c5d4c9a7511/ python3 << 'EOF' import pdfplumber import os files = [ "DOC-20260413-WA0011_260802234828.pdf", "DOC-20260413-WA0012_260802234820.pdf", "DOC-20260709-WA0006_260802234807.pdf", "LNCT 2nd sessional paper (1).pdf", "1st sessional 3rd prof Lnct (1).pdf", "3rd year ln paper (1).pdf", "3rd prof university paper Lnct(2).pdf" ] base = "/tmp/workspace/attachments/b0ed0c88-fa80-49e2-b122-1c5d4c9a7511/" for f in files: path = base + f print(f"\n{'='*60}") print(f"FILE: {f}") print('='*60) try: with pdfplumber.open(path) as pdf: print(f"Pages: {len(pdf.pages)}") for i, page in enumerate(pdf.pages): text = page.extract_text() if text: print(f"\n--- Page {i+1} ---") print(text) else: print(f"\n--- Page {i+1}: [No text extracted] ---") except Exception as e: print(f"ERROR: {e}") EOF

Several files are scanned images (not text-extractable). Let me use analyse_media on the scanned pages and also the other PDFs that returned empty text. I'll handle the image-based ones and the scanned 50-page file.
analyse_media
analyse_media
The scanned PDFs can't be analysed via URL (local files). Let me try using Python to render them as images and extract text using PIL/pytesseract, or try markitdown for OCR.
Running Command

python3 -c " import subprocess result = subprocess.run(['python3', '-c', 'import markitdown; print(\"ok\")'], capture_output=True, text=True) print(result.stdout, result.stderr) result2 = subprocess.run(['python3', '-c', 'import pytesseract; print(\"ok\")'], capture_output=True, text=True) print('pytesseract:', result2.stdout, result2.stderr) result3 = subprocess.run(['python3', '-c', 'import fitz; print(\"ok\")'], capture_output=True, text=True) print('pymupdf/fitz:', result3.stdout, result3.stderr) "

Running Command

BASE="/tmp/workspace/attachments/b0ed0c88-fa80-49e2-b122-1c5d4c9a7511" # Try markitdown on the scanned/empty PDFs python3 << 'EOF' from markitdown import MarkItDown md = MarkItDown() files = [ "LNCT 2nd sessional paper (1).pdf", "1st sessional 3rd prof Lnct (1).pdf", "3rd prof university paper Lnct(2).pdf", "3rd year ln paper (1).pdf", ] base = "/tmp/workspace/attachments/b0ed0c88-fa80-49e2-b122-1c5d4c9a7511/" for f in files: path = base + f print(f"\n{'='*60}") print(f"FILE: {f}") print('='*60) try: result = md.convert(path) text = result.text_content if text and text.strip(): print(text[:3000]) else: print("[No text extracted by markitdown]") except Exception as e: print(f"ERROR: {e}") EOF

These 4 PDFs are pure image scans - no text layer. Let me convert them to images using PIL/pypdf and then use fetch_image to read them visually.
Running Command

python3 << 'EOF' from pypdf import PdfReader from PIL import Image import io, os base = "/tmp/workspace/attachments/b0ed0c88-fa80-49e2-b122-1c5d4c9a7511/" outdir = "/tmp/workspace/psm_images/" os.makedirs(outdir, exist_ok=True) files = [ "LNCT 2nd sessional paper (1).pdf", "1st sessional 3rd prof Lnct (1).pdf", "3rd prof university paper Lnct(2).pdf", ] for fname in files: path = base + fname prefix = fname.replace(".pdf","").replace(" ","_").replace("(","").replace(")","") reader = PdfReader(path) print(f"\n{fname}: {len(reader.pages)} pages") for i, page in enumerate(reader.pages): for j, img_obj in enumerate(page.images): img_data = img_obj.data img = Image.open(io.BytesIO(img_data)) out_path = f"{outdir}{prefix}_page{i+1}_img{j+1}.png" img.save(out_path) print(f" Saved: {out_path} ({img.size})") EOF

Images extracted. Now let me view them using fetch_image to read all questions from the image-based PDFs. I'll prioritize the PSM-specific ones and do multiple pages in parallel.
Loading Image
Loading Image
Loading Image
Loading Image
fetch_image doesn't work with local files. Let me serve these images via a simple HTTP server and use analyse_media, OR use read_file to read the images as base64. Actually, I can use the execute to start a local server and then use analyse_media. But the simplest approach is to use Python's PIL to do OCR-like text reading. Let me try using pdf2image or render each page via pypdf's image extraction and then use a different approach.
Actually, I already have the images saved. Let me try to serve them on a local port and use analyse_media with localhost URLs.
Running Command

# Start a simple HTTP server in background to serve images cd /tmp/workspace/psm_images/ python3 -m http.server 8765 & sleep 2 echo "Server started, PID: $!" ls /tmp/workspace/psm_images/

analyse_media
analyse_media
The analyse_media tool can't reach localhost. I've already extracted text from 3 of the 7 PDFs successfully (the text-based ones). The remaining 4 PDFs are scanned images that can't be OCR'd without tesseract. Let me now compile the comprehensive chapter-wise analysis from the 3 text-extractable papers, plus note which papers couldn't be read.
Let me check the 50-page "3rd year ln paper" - it may have some pages with text among the scans, and also try the 3rd prof university paper with a different approach.
Running Command

python3 << 'EOF' # Try extracting images from the 50-page scanned PDF from pypdf import PdfReader import io, os base = "/tmp/workspace/attachments/b0ed0c88-fa80-49e2-b122-1c5d4c9a7511/" outdir = "/tmp/workspace/psm_images/" fname = "3rd year ln paper (1).pdf" path = base + fname reader = PdfReader(path) print(f"{fname}: {len(reader.pages)} pages") # Extract first few pages images to check what's there for i in range(min(5, len(reader.pages))): page = reader.pages[i] imgs = list(page.images) print(f" Page {i+1}: {len(imgs)} images") for j, img_obj in enumerate(imgs): from PIL import Image img = Image.open(io.BytesIO(img_obj.data)) out_path = f"{outdir}ln3rd_page{i+1}_img{j+1}.png" img.save(out_path) print(f" Saved: {out_path} ({img.size})") EOF

I have very limited analyse_media calls left (only 1 remaining) and fetch_image can't access local files. The scanned PDFs cannot be OCR'd in this environment without tesseract.
I have comprehensive text from 3 papers. Let me now compile the full chapter-wise PSM topic analysis from the text I already extracted.Here is the full chapter-wise analysis of all PSM topics that appeared across your papers. I was able to extract text from 3 papers fully (1st Sessional 2026, 2nd Sessional 2026, and Pre-University Exam 2026). The remaining 4 PDFs are scanned image files (no text layer) and cannot be OCR-processed in this environment - I'll flag those at the end.

PSM TOPICS - CHAPTER-WISE (from readable papers)


📌 PAPER SOURCE KEY

  • P1 = 1st Sessional (09/02/2026)
  • P2 = 2nd Sessional (11/04/2026)
  • P3 = Pre-University Exam Paper 1 (07/07/2026)
  • P4 = Pre-University Exam Paper 2 (08/07/2026)

1. EPIDEMIOLOGY (Concepts & Methods)

TopicPaperType
Case-control study (steps, OR, advantages, limitations, biases)P2LAQ
Randomized Controlled Trial - all steps; Vaccine trialP3LAQ
Iceberg phenomenonP1SAQ
Epidemiological determinants of measles + clinical features + preventionP1SAQ
Screening - definition, criteria, characteristics, advantages, limitationsP2, P3LAQ
Difference: Screening test vs Diagnostic testP2SAQ
Steps of investigation of an epidemicP2SAQ
Sensitivity & Specificity (numerical: EEG & brain tumour data)P2VSAQ
Incidence & Prevalence (definitions)P2VSAQ
Epizootic & Epornithic (definitions)P2VSAQ
Confounding - definition & how to removeP3VSAQ
AFP SurveillanceP3SAQ
Integrated Disease Surveillance Project (IDSP)P3SAQ
Vital events registrationP3VSAQ
Emerging diseases and examplesP3VSAQ
Phases in Public HealthP3SAQ

2. BIOSTATISTICS

TopicPaperType
Gaussian (Normal) distribution - definitionP1SAQ
Bar chart - definitionP1VSAQ
Histogram & Bar chart (difference/definition)P2VSAQ
Sampling - definition, methodsP2SAQ
Types of learningP2SAQ

3. COMMUNICABLE DISEASES

Diarrhoeal Disease

TopicPaperType
Case scenario: rice-watery diarrhoea (diagnosis, types, management, epidemiology, preventive measures)P1LAQ
Epidemiology & control of Tuberculosis in IndiaP1SAQ
Post-Exposure Prophylaxis (PEP) of Rabies - bite classification, wound management, vaccine regimen, immunoglobulinP1SAQ
Epidemiology of Leprosy (Indian classification, Madrid classification, prevention & control)P2SAQ
National Vector Borne Disease Control ProgrammeP1LAQ
Polio-free India / "Polio free by 2030"P3SAQ
National Leprosy Eradication Programme (organizational structure, initiatives, strategy)P3SAQ
Amplifier host in zoonosesP3VSAQ
Major malaria activities for areas with API > 5P3VSAQ
Weil's disease (definition)P1VSAQ
Screening of cervical cancerP3SAQ

4. NON-COMMUNICABLE DISEASES & CHRONIC CONDITIONS

TopicPaperType
Classification of BP; Risk factors for hypertension & its prevention (case scenario: obese smoker with 150/100 mmHg)P3LAQ
Epidemiology of lead poisoning - sources, clinical features, preventionP2SAQ
Occupational diseases classification; Pneumoconiosis - detail + preventionP2SAQ
Occupational hazards to medical professionalsP4SAQ

5. NUTRITION

TopicPaperType
Nutritional problems in public healthP1SAQ
Methods of assessment of nutritional status; WHO recommendations for prevention of PEM in communityP4LAQ
Food fortification with two examplesP1VSAQ
ICDS - beneficiaries & servicesP1, P4SAQ

6. ENVIRONMENT & WATER

TopicPaperType
Water quality criteria & standardsP1SAQ
Break-point chlorinationP1VSAQ
Methods of refuse/solid waste disposalP1SAQ
Criteria for ideal disinfectant for waterP4VSAQ

7. DEMOGRAPHY & VITAL STATISTICS

TopicPaperType
Vital events registrationP3VSAQ
Causes of high Maternal Mortality in India + preventive measures to bring down MMRP4SAQ
Enumerate Biomedical Waste categoriesP4SAQ

8. SOCIAL & PREVENTIVE MEDICINE (General)

TopicPaperType
Determinants of health - role of family, education & socioeconomic statusP2SAQ
Social problems in societyP2SAQ
Acculturation & social pathologyP4VSAQ
Eugenics & EuthenicsP4VSAQ

9. NATIONAL HEALTH PROGRAMMES

TopicPaperType
National Vector Borne Disease Control Programme (NVBDCP)P1LAQ
National Immunization ScheduleP3SAQ
National Leprosy Eradication ProgrammeP3SAQ
ICDS - beneficiaries & servicesP1, P4SAQ
Cold chainP2SAQ

10. HEALTH PLANNING & MANAGEMENT

TopicPaperType
Planning cycle; Management methods & techniquesP4LAQ
Phases in Public HealthP3SAQ
Indian Public Health Standard for Community Health CenterP4SAQ
TriageP3SAQ
NITI AayogP4VSAQ
Functions of UNICEFP4VSAQ
UNICEF (definition/role)P1VSAQ
Doctor-Patient RelationshipP3SAQ

11. REPRODUCTIVE & CHILD HEALTH

TopicPaperType
Methods of contraceptionP4SAQ
Causes of high MMR in India + preventionP4SAQ
Beneficiaries & services under ICDSP1, P4SAQ
Haddon's Matrix Model (injury epidemiology)P1SAQ
Child Mental Health / Behavioural problems in children + Child Welfare Rules 2016 (color coding)P4SAQ

12. PRIMARY HEALTH CARE

TopicPaperType
Definition of PHC; Principles of PHC with examplesP4SAQ
Indian Public Health Standard for CHCP4SAQ
Community Health Center standardsP4SAQ

13. MENTAL HEALTH

TopicPaperType
Types of mental illness, causes & preventive measuresP4SAQ

14. HEALTH EDUCATION & COMMUNICATION

TopicPaperType
Methods of communicationP4SAQ
Types of learningP2SAQ

TOPICS REPEATED ACROSS MULTIPLE PAPERS (HIGH PRIORITY)

TopicPapers
Screening (definition, criteria, types)P2, P3
ICDS - beneficiaries & servicesP1, P4
Leprosy (classification + control)P2, P3

⚠️ PAPERS NOT READABLE (Scanned Images - No OCR Available)

The following 4 files contain only scanned images with no text layer and could not be processed:
  1. LNCT 2nd sessional paper (1).pdf - 2 pages (scanned)
  2. 1st sessional 3rd prof Lnct (1).pdf - 4 pages (scanned)
  3. 3rd prof university paper Lnct(2).pdf - 3 pages (scanned)
  4. 3rd year ln paper (1).pdf - 50 pages (scanned via CamScanner)
To read these, you would need to share clearer copies or re-upload them as typed/digital PDFs. Alternatively, if you can tell me the exam names/dates for these 4 papers, I can help identify their likely topics manually.
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