Miliary tb full details for md exam answer 15 marks from this pdf and your data
cd /home/daytona/workspace/attachments/fe64be5e-2b98-4f43-8585-ca6b26bea322 && python3 -c " import pdfplumber import re pdf_path = 'Sharma mohan latest_compressed-compressed (1).pdf' with pdfplumber.open(pdf_path) as pdf: print(f'Total pages: {len(pdf.pages)}') # Search for miliary TB pages for i, page in enumerate(pdf.pages): text = page.extract_text() if text and ('miliary' in text.lower() or 'Miliary' in text): print(f'--- PAGE {i+1} ---') print(text[:3000]) print() "
python3 -c " import pdfplumber pdf_path = '/home/daytona/workspace/attachments/fe64be5e-2b98-4f43-8585-ca6b26bea322/Sharma mohan latest_compressed-compressed (1).pdf' with pdfplumber.open(pdf_path) as pdf: print(f'Total pages: {len(pdf.pages)}') for i, page in enumerate(pdf.pages): text = page.extract_text() if text and ('miliary' in text.lower() or 'Miliary' in text): print(f'=== PAGE {i+1} ===') print(text[:4000]) print() " 2>&1 | head -200
python3 << 'EOF' import pdfplumber pdf_path = '/home/daytona/workspace/attachments/fe64be5e-2b98-4f43-8585-ca6b26bea322/Sharma mohan latest_compressed-compressed (1).pdf' with pdfplumber.open(pdf_path) as pdf: print(f'Total pages: {len(pdf.pages)}') miliary_pages = [] for i, page in enumerate(pdf.pages): text = page.extract_text() if text and ('miliary' in text.lower()): miliary_pages.append(i+1) print(f'Pages with miliary: {miliary_pages}') EOF
python3 << 'PYEOF' import pdfplumber, sys pdf_path = '/home/daytona/workspace/attachments/fe64be5e-2b98-4f43-8585-ca6b26bea322/Sharma mohan latest_compressed-compressed (1).pdf' try: with pdfplumber.open(pdf_path) as pdf: total = len(pdf.pages) print(f'Total pages: {total}') results = [] for i, page in enumerate(pdf.pages): text = page.extract_text() or '' if 'miliary' in text.lower(): results.append((i+1, text)) print(f'Miliary pages: {[r[0] for r in results]}') for pg, txt in results: print(f'\n===PAGE {pg}===') print(txt[:5000]) except Exception as e: print(f'Error: {e}', file=sys.stderr) PYEOF
| Symptom | Frequency |
|---|---|
| Fever (often high-grade, remittent) | Very common |
| Anorexia and weight loss | Very common |
| Night sweats | Common |
| Malaise, weakness | Common |

| Biopsy Site | Yield |
|---|---|
| Liver (AFB smear + culture + PCR) | Highest yield |
| Bone marrow | High (especially if pancytopenic) |
| Lymph node | Useful if lymphadenopathy present |
| Transbronchial | Useful with bronchoscopy |
| Condition | Features |
|---|---|
| Histoplasmosis | Fungal; endemic areas; serology helpful |
| Sarcoidosis | Perilymphatic nodules; raised ACE; non-caseating granulomas |
| Miliary metastases | Malignancy history; variable nodule size |
| Pneumoconiosis (siderosis) | Occupational history |
| Disseminated fungal infection | Cryptococcus, Coccidioides |
| Langerhans cell histiocytosis | Upper lobe predominance |
| Drug | Daily Dose |
|---|---|
| Isoniazid (H) | 5 mg/kg (max 300 mg) |
| Rifampicin (R) | 10 mg/kg (max 600 mg) |
| Pyrazinamide (Z) | 25 mg/kg (max 2 g) |
| Ethambutol (E) | 15 mg/kg (max 1.6 g) |
Initiate treatment immediately on clinical suspicion — do NOT wait for microbiological confirmation. Delay in treatment is the leading cause of the high mortality (~21%) in miliary TB.
| Feature | Key Point |
|---|---|
| Definition | Haematogenous dissemination of MTB to multiple organs |
| Lesion | 1–3 mm granulomas resembling millet seeds |
| Pathognomonic sign | Choroidal tubercles on fundoscopy |
| CXR | Bilateral diffuse 1–3 mm nodules ("snowstorm"); absent in 50% early |
| TST/Mantoux | Often false negative (anergy) |
| Best diagnostic yield | Liver biopsy + AFB smear/culture/PCR |
| Treatment | 2HRZE + 7–10 HR; steroids if meningitis/ARDS |
| Mortality | ~21% |