Target Product Profile of a Molecular Drug-Susceptibility Test for Use in Microscopy Centers
Bibliographic record
Abstract
BACKGROUND: Current phenotypic testing for drug resistance in patients with tuberculosis is inadequate primarily with respect to turnaround time. Molecular tests hold the promise of an improved time to diagnosis. METHODS: A target product profile for a molecular drug-susceptibility test (DST) was developed on the basis of a collaborative effort that included opinions gathered from researchers, clinicians, policy makers, and test developers on optimal clinical and operational characteristics in settings of intended use. In addition, the current diagnostic ecosystem and the diagnostic development landscape were mapped. RESULTS: Molecular DSTs for detecting tuberculosis in microscopy centers should ideally evaluate for resistance to rifampin, fluoroquinolones, isoniazid, and pyrazinamide and enable the selection of the most appropriate treatment regimen. Performance characteristics of DSTs need to be optimized, but compromises can be made that depend on the trade-off between a false-positive result and a false-negative result. The operational requirements of a test will vary depending on the site of implementation. However, the most-important considerations pertain to quality control, maintenance and calibration, and the ability to export data. CONCLUSION: This target product profile defines the needs as perceived by the tuberculosis stakeholder community and attempts to provide a means of communication with test developers to ensure that fit-for-purpose DSTs are being developed.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".