Impact of Molecular Diagnostics for Tuberculosis on Patient-Important Outcomes: A Systematic Review of Study Methodologies
Bibliographic record
Abstract
BACKGROUND: Several reviews on the accuracy of Tuberculosis (TB) Nucleic Acid Amplification Tests (NAATs) have been performed but the evidence on their impact on patient-important outcomes has not been systematically reviewed. Given the recent increase in research evaluating such outcomes and the growing list of TB NAATs that will reach the market over the coming years, there is a need to bring together the existing evidence on impact, rather than accuracy. We aimed to assess the approaches that have been employed to measure the impact of TB NAATs on patient-important outcomes in adults with possible pulmonary TB and/or drug-resistant TB. METHODS: We first develop a conceptual framework to clarify through which mechanisms the improved technical performance of a novel TB test may lead to improved patient outcomes and outline which designs may be used to measure them. We then systematically review the literature on studies attempting to assess the impact of molecular TB diagnostics on such outcomes and provide a narrative synthesis of designs used, outcomes assessed and risk of bias across different study designs. RESULTS: We found 25 eligible studies that assessed a wide range of outcomes and utilized a variety of experimental and observational study designs. Many potentially strong design options have never been used. We found that much of the available evidence on patient-important outcomes comes from a small number of settings with particular epidemiological and operational context and that confounding, time trends and incomplete outcome data receive insufficient attention. CONCLUSIONS: A broader range of designs should be considered when designing studies to assess the impact of TB diagnostics on patient outcomes and more attention needs to be paid to the analysis as concerns about confounding and selection bias become relevant in addition to those on measurement that are of greatest concern in accuracy studies.
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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 | 0.003 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".