Selective reporting of antibiotic susceptibility testing results: a promising antibiotic stewardship tool
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction Selective reporting of antibiotic susceptibility testing (AST) results is a potentially interesting tool for antibiotic stewardship. It consists of performing AST according to usual practices, but the results are reported to the prescriber only for a few antibiotics (i.e. first-line agents) or not reported at all when colonization is likely.Areas covered We retrieved 20 studies exploring the impact of selective reporting. Overall, selective reporting is able to influence antibiotic use, both discouraging prescription in case of colonization, and promoting the selection of narrow-spectrum agents. Most studies concerned urine samples. Evidence on the impact on antibiotic resistance is insufficient. Unintended consequences were not observed, but evidence on this topic is scarce. Selective reporting is well implemented in a few countries, and a huge heterogeneity of practices exists.Expert opinion Evidence shows that selective reporting can help reducing inappropriate and unnecessary antibiotic prescriptions. Uncomplicated urinary tract infections are probably the best initial target, both in hospital and community settings, but other non-severe infections can be a suitable option. The implementation of selective reporting should be promoted by the scientific community, with detailed practical guidelines, and its impact should be further assessed in large interventional studies.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it