Delays in diagnosis and treatment of pulmonary tuberculosis in India: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To systematically review Indian literature on delays in tuberculosis (TB) diagnosis and treatment. METHODS: We searched multiple sources for studies on delays in patients with pulmonary TB and those with chest symptoms. Studies were included if numeric data on any delay were reported. Patient delay was defined as the interval between onset of symptoms and the patient's first contact with a health care provider. Diagnostic delay was defined as the interval between the first consultation with a health care provider and diagnosis. Treatment delay was defined as the interval between diagnosis and initiation of anti-tuberculosis treatment. Total delay was defined as time interval from the onset of symptoms until treatment initiation. RESULTS: Among 541 potential citations identified, 23 studies met the inclusion criteria. Included studies used a variety of definitions for onset of symptoms and delays. Median estimates of patient, diagnostic and treatment delay were respectively 18.4 (IQR 14.3-27.0), 31.0 (IQR 24.5-35.4) and 2.5 days (IQR 1.9-3.6) for patients with TB and those with chest symptoms combined. The median total delay was 55.3 days (IQR 46.5-61.5). About 48% of all patients first consulted private providers; an average of 2.7 health care providers were consulted before diagnosis. Number and type of provider first consulted were the most important risk factors for delay. CONCLUSIONS: These findings underscore the need to develop novel strategies for reducing patient and diagnostic delays and engaging first-contact health care providers.
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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 | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".