Five steps to high quality antimicrobial stewardship research
Bibliographic record
Abstract
Abstract The escalating threat of antimicrobial resistance (AMR) necessitates impactful, reproducible, and scalable antimicrobial stewardship strategies. This review addresses the critical need to enhance the quality of antimicrobial stewardship intervention research. We propose five considerations for authors planning and evaluating antimicrobial stewardship initiatives. Antimicrobial stewards should consider the following mnemonic ABCDE: (A) plan Ahead using implementation science; (B) Be clear and thoroughly describe the intervention by using the TidIER checklist; (C) Use a Checklist to comprehensively report study components; (D) Select a study Design carefully; and (E) Assess Effectiveness and implementation by selecting meaningful outcomes. Incorporating these recommendations will help strengthen the evidence base of antimicrobial stewardship literature and support optimal implementation of strategies to mitigate AMR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.582 | 0.605 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.029 | 0.038 |
| Open science | 0.011 | 0.025 |
| Research integrity | 0.023 | 0.044 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".