Tuberculosis treatment monitoring tests during routine practice: study design guidance
Bibliographic record
Abstract
SCOPE: The current tools for tuberculosis (TB) treatment monitoring, smear microscopy and culture, cannot accurately predict poor treatment outcomes. Research into new TB treatment monitoring tools (TMTs) is growing, but data are unreliable. In this article, we aim to provide guidance for studies investigating and evaluating TB TMT for use during routine clinical care. Here, a TB TMT would guide treatment during the course of therapy, rather than testing for a cure at the regimen's end. This article does not cover the use of TB TMTs as surrogate endpoints in the clinical trial context. METHODS: Guidelines were initially informed by experiences during a systematic review of TB TMTs. Subsequently, a small content expert group was consulted for feedback on initial recommendations. After revision, feedback from substantive experts across sectors was sought. QUESTIONS ADDRESSED BY THE GUIDELINE AND RECOMMENDATIONS: The proposed considerations and recommendations for studies evaluating TB TMTs for use during the treatment in routine clinical care fall into eight domains. We provide specific recommendations regarding study design and recruitment, outcome definitions, reference standards, participant follow-up, clinical setting, study population, treatment regimen reporting, and index tests and data presentation. Overall, TB TMTs should be evaluated in a manner similar to diagnostic tests, but TB TMT accuracy must be assessed at multiple timepoints throughout the treatment course, and TB TMTs should be evaluated in study populations who have already received a diagnosis of TB. Study design and outcome definitions must be aligned with the developmental phase of the TB TMT under evaluation. There is no reference standard for TB treatment response, so different reference standards and comparator tests have been proposed, the selection of which will vary depending on the developmental phase of the TMT under assessment. The use of comparator tests can assist in generating evidence. Clarity is required when reporting of timepoints, TMT read-outs, and analysis results. Implementing these recommendations will lead to higher quality TB TMT studies that will allow data to be meaningfully compared, thereby facilitating the development of novel tools to guide individual TB therapy and improve treatment outcomes.
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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 | 0.040 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".