Selective reporting of antibiotic susceptibility testing results: a promising antibiotic stewardship tool
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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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 | 0.027 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".