Rapid TB diagnostic service and community action to FIND.TREAT.ALL#EndTB, Maputo, Mozambique, 2013–2018
Bibliographic record
Abstract
Finding and treating all tuberculosis (TB) patients is crucial for ending TB. We investigated whether rapid diagnostic turnaround time (TAT) and patient tracking could increase TB treatment initiation in Maputo, Mozambique. Among 3329 TB patients newly diagnosed by the University Eduardo Mondlane-Anti-Persoonsmijnen Ontmijnende Product Ontwikkeling/Anti-Personnel Landmines Detection Product Development (APOPO) Laboratory between 2013 and 2018, on average 61% were verifiably linked to care. This proportion increased from 54% (first half 2013) to 79% (second half 2018) after introducing a 24-hour TAT in 2015 and patient tracking conducted by a community-based partner, Associação Kenguelekezé, in 2017. Rapid, well-connected TB diagnostic services can reduce pre-treatment loss to follow-up and support the joint initiative of WHO, Stop TB and Global Fund to 'FIND.TREAT.ALL.#EndTB'.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".