Clinical standards for antimicrobial stewardship in TB care
Bibliographic record
Abstract
BACKGROUND: While antimicrobial stewardship (AMS) is essential for combating antimicrobial resistance (AMR), TB-specific AMS strategies remain poorly defined. METHODS: An international panel of 62 experts participated in a Delphi process. Using a 5-point Likert scale (5 = strong agreement; 1 = strong disagreement), participants evaluated 10 draft clinical standards developed by a core coordination team. A standard was adopted if ≥90% of respondents rated it three or higher, according to a predefined consensus threshold. RESULTS: All 10 standards reached the consensus threshold and were adopted: Standard 1, integration of TB into national AMR action plans; Standard 2, implementation of TB surveillance systems; Standard 3, education of health care providers, individuals affected by TB, and the public; Standard 4, integration of TB into AMS activities; Standard 5, establishment of expert consultation services; Standard 6, targeted testing and preventive treatment for individuals at risk for TB; Standard 7, access to timely and comprehensive drug susceptibility testing; Standard 8, prioritisation of efficacy, safety, and resistance prevention in TB treatment regimens; Standard 9, clinical and microbiological monitoring of treatment response; and Standard 10, assessment of adherence, drug exposure, and resistance in treatment failure. CONCLUSION: These clinical standards aim to support clinicians, programme managers, and public health authorities in implementing effective, TB-specific AMS strategies.
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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 | 0.296 | 0.312 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".