Are infection specialists recommending short antibiotic treatment durations? An ESCMID international cross-sectional survey
Bibliographic record
Abstract
Objectives: To evaluate the current practice and the willingness to shorten the duration of antibiotic therapy among infection specialists. Methods: Infection specialists giving at least weekly advice on antibiotic prescriptions were invited to participate in an online cross-sectional survey between September and December 2016. The questionnaire included 15 clinical vignettes corresponding to common clinical cases with favourable outcomes; part A asked about the antibiotic treatment duration they would usually advise to prescribers and part B asked about the shortest duration they were willing to recommend. Results: We included 866 participants, mostly clinical microbiologists (22.8%, 197/863) or infectious diseases specialists (58.7%, 507/863), members of an antibiotic stewardship team in 73% (624/854) of the cases, coming from 58 countries on all continents. Thirty-six percent of participants (271/749) already advised short durations of antibiotic therapy (compared with the literature) to prescribers for more than half of the vignettes and 47% (312/662) chose shorter durations in part B compared with part A for more than half of the vignettes. Twenty-two percent (192/861) of the participants declared that their regional/national guidelines expressed durations of antibiotic therapy for a specific clinical situation as a fixed duration as opposed to a range and in the multivariable analysis this was associated with respondents advising short durations for more than half of the vignettes (adjusted OR 1.5, P = 0.02). Conclusions: The majority of infection specialists currently do not advise the shortest possible duration of antibiotic therapy to prescribers. Promoting short durations among these experts is urgently needed.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".