Predictors of Treatment Outcome for Retreatment Pulmonary Tuberculosis Cases among Tribal People of an Eastern India District: A Prospective Cohort Study
Bibliographic record
Abstract
Objective. The study was conducted to assess the treatment outcome of different category retreatment cases with the aim of finding out the important predictors of unfavorable outcomes. Methodology. This hospital based prospective cohort study was conducted in three tuberculosis units (TUs) of west Midnapore (a district of Eastern India), covering mostly the tribal populated areas. Patients who were registered for Category II antituberculosis treatment between 1st quarter of 2013 (Jan to Mar) and 4th quarter of 2013 (Oct to Dec) were considered as our study cohort and they were followed up till December 2014. The study was started with 177 patients but ultimately ended with 165 patients. Results. Unfavorable outcome was observed among 24.8% patients. Among them mostly 51.2% were defaulter, 22% were failure case, and 26.8% died during treatment. Patients, who were minority by religion, were found 4 times more vulnerable for unfavorable outcome. Unfavorable outcome was found 7 times more common among retreatment TB cases who remain sputum positive after completion of initiation phase of Category II treatment. Conclusion. Programmatic approach should be specified to address the minority by religion population and to reduce the load of sputum positive cases after completion of initiation phase treatment by tracking them.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".