Standards for clinical trials for treating TB
Bibliographic record
Abstract
BACKGROUND: The value, speed of completion and robustness of the evidence generated by TB treatment trials could be improved by implementing standards for best practice. METHODS: A global panel of experts participated in a Delphi process, using a 7-point Likert scale to score and revise draft standards until consensus was reached. RESULTS: Eleven standards were defined: Standard 1, high quality data on TB regimens are essential to inform clinical and programmatic management; Standard 2, the research questions addressed by TB trials should be relevant to affected communities, who should be included in all trial stages; Standard 3, trials should make every effort to be as inclusive as possible; Standard 4, the most efficient trial designs should be considered to improve the evidence base as quickly and cost effectively as possible, without compromising quality; Standard 5, trial governance should be in line with accepted good clinical practice; Standard 6, trials should investigate and report strategies that promote optimal engagement in care; Standard 7, where possible, TB trials should include pharmacokinetic and pharmacodynamic components; Standard 8, outcomes should include frequency of disease recurrence and post-treatment sequelae; Standard 9, TB trials should aim to harmonise key outcomes and data structures across studies; Standard 10, TB trials should include biobanking; Standard 11, treatment trials should invest in capacity strengthening of local trial and TB programme staff. CONCLUSION: These standards should improve the efficiency and effectiveness of evidence generation, as well as the translation of research into policy and practice.
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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 | 0.772 | 0.822 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.033 | 0.013 |
| Open science | 0.021 | 0.020 |
| Research integrity | 0.046 | 0.054 |
| Insufficient payload (model declined to judge) | 0.012 | 0.015 |
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".