Bibliographic record
Abstract
Microscopic-observation drug susceptibility and thin layer agar assays for the detection of drug resistant tuberculosis: a systematic review and meta-analysis Jessica Minion, Erika Leung, Dick Menzies, Madhukar Pai Summary Background Simple, rapid, and aff ordable tests are needed to detect drug resistance in Mycobacterium tuberculosis. We did a systematic review and meta-analysis to investigate the accuracy of microscopic-observation drug susceptibility (MODS) and thin layer agar (TLA) assays for rapid screening of patients at risk of drug-resistant tuberculosis. Methods In accordance with protocols and methods recommended by the Cochrane Diagnostic Test Accuracy Working Group, we systematically searched PubMed, Embase, and Biosis for reports published between January, 1990, and February, 2009. We included studies investigating detection of drug resistance in M tuberculosis with the MODS or TLA assay, and in which an accepted reference standard was used. Data extracted from the studies were combined by use of bivariate random-eff ects regression models and hierarchical summary receiver operating characteristic curves to estimate sensitivity and specifi city for detection of resistance to specifi c drugs. Findings We identifi ed 12 studies, of which nine investigated the MODS assay and three investigated the TLA assay.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.641 | 0.288 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".