Turning Off the Tap: Using the FAST Approach to Stop the Spread of Drug-Resistant Tuberculosis in the Russian Federation
Bibliographic record
Abstract
Background: We report the association of the FAST strategy (find cases actively, separate safely, and treat effectively) with reduction of hospital-based acquisition of multidrug-resistant tuberculosis in the Russian Federation. Methods: We used preintervention and postintervention cohorts in 2 Russian hospitals to determine whether the FAST strategy was associated with a reduced odds of converting MDR tuberculosis within 12 months among patients with tuberculosis susceptible to isoniazid and rifampin at baseline. Results: Sixty-three of 709 patients (8.9%) with isoniazid and rifampin-susceptible tuberculosis acquired MDR tuberculosis; 55 (12.2%) were in the early cohort, and 8 (3.1%) were in the FAST cohort. The FAST strategy was associated with a reduced odds (adjusted odds ratio, 0.16; 95% confidence interval, .07-.39) and 9.2% absolute reduction in the risk of MDR tuberculosis acquisition. Conclusion: Use of the FAST strategy in 2 Russian hospitals was associated with significantly less MDR tuberculosis 12 months after implementation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".