Documenting the indication for antimicrobial prescribing: A retrospective observational study of long‐term care homes
Bibliographic record
Abstract
BACKGROUND: Overuse of antimicrobials in residents of long-term care homes is common and can result in harm. Antimicrobial stewardship interventions are needed in the long-term care (LTC) homes setting to improve the appropriate use of antimicrobials. Previous literature has highlighted the importance of documenting antimicrobial indication as a strategy that contributes to improve antimicrobial use; however, there is a lack of evidence in LTC homes. This study examines the prevalence, clarity, and facility-level variability of antibiotic indication documentation in this setting. METHODS: This is an observational retrospective study of oral antibiotic prescriptions dispensed to 218 homes between January 1, 2021 and December 31, 2022 in Ontario, Canada. Indication was obtained from reviewing antibiotic prescription data. Clarity was determined by comparing documented indication to the National Antimicrobial Prescribing Survey (NAPS). Descriptive analysis was performed to examine the prevalence and clarity of indication documentation. Funnel plots were generated to examine variability in prevalence of indication documentation and clarity at the home level. RESULTS: Overall, 22.9% (7998/34,867) of prescriptions had an indication documented. The proportion of indications that were clear was 37% (2984/7998). The most common indications were for urinary (45%), skin and soft tissue (19.9%) and respiratory infections (15.0%). At the home level, the median prevalence of indication was 19.6% (interquartile range [IQR]: 10.8%-31.4%) and median prevalence of clear indications was 35.1% (IQR: 23.8%-42.9%). Funnel plots revealed substantial variability in indication prevalence with 46.3% of homes falling outside of 99% limits but minimal variability in indication clarity between homes with only 8.7% of homes outside of 99% control limits. CONCLUSIONS: There is an opportunity to increase both the prevalence and clarity of antibiotic prescriptions in LTC homes. Future work should focus on determining how best to support prescription indication documentation in this setting with consideration being given to prescription workflow and most common antibiotic prescription indications.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".