Which New Diagnostics for Tuberculosis, and When?
Bibliographic record
Abstract
Recently, new diagnostic tools for tuberculosis detection and resistance testing have become available. The World Health Organization endorses new tuberculosis diagnostics by using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) process. This endorsement process takes place when limited evidence beyond test accuracy is available. There is a need to provide guidance to tuberculosis programs about which new diagnostics to scale up and how best to position them in diagnostic algorithms. To speed adoption of new diagnostics for tuberculosis, the policy recommendation process should be revised to consist of 2 steps: technical recommendation and programmatic recommendation. Technical recommendation would follow the GRADE process and be based on accuracy with limited cost and feasibility data, while programmatic recommendation would include patient-important outcomes, cost-effectiveness when implemented under routine conditions, and factors critical to successful scale-up. The evidence for both steps should be systematically collected, but each requires different study designs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.026 | 0.011 |
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".