Treatment as diagnosis and diagnosis as treatment: empirical management of presumptive tuberculosis in India
Bibliographic record
Abstract
BACKGROUND: Mismanagement of TB is a concern in the Indian private sector, and empirical management might be a key contributor. OBJECTIVE: To understand factors associated with empirical diagnosis and treatment of presumed TB in India's private sector and examine their effects on TB care. DESIGN: In this ethnographic study, 110 private practitioners of varying qualification who interacted with TB patients (90 in Mumbai and 20 in Patna) were interviewed, and a subset was observed while providing clinical care. Interviews and observations were analysed for indicators of empirical diagnosis and treatment. RESULTS: All non-specialist practitioners began antibiotic treatment, especially quinolones, for persistent cough before prescribing a test. Several factors contribute to empirical management. These include a common practice use of medications as diagnostic tools, a desire to provide rapid symptom relief to patients, a desire to manage illness costs effectively, uncertainty about the presentation of TB, the effects of broad spectrum antibiotics on TB symptomology, and uncertainty about the accuracy of available TB tests. CONCLUSION: Empiricism in general and in TB care is widespread in the urban private sector in India. Ethnography might offer useful insights for addressing this in public-private mix models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".