Influences on the start, selection and duration of treatment with antibiotics in long-term care facilities
Bibliographic record
Abstract
BACKGROUND: Understanding the extent to which current antibiotic prescribing behaviour is influenced by clinicians' historical patterns of practice will help target interventions to optimize antibiotic use in long-term care. Our objective was to evaluate whether clinicians' historical prescribing behaviours influence the start, prolongation and class selection for treatment with antibiotics in residents of long-term care facilities. METHODS: We conducted a retrospective cohort study of all physicians who prescribed to residents in long-term care facilities in Ontario between Jan. 1 and Dec. 31, 2014. We examined variability in antibiotic prescribing among physicians for 3 measures: start of treatment with antibiotics, use of prolonged durations exceeding 7 days and selection of fluoroquinolones. Funnel plots with control limits were used to determine the extent of variation and characterize physicians as extreme low, low, average, high and extreme high prescribers for each tendency. Multivariable logistic regression was used to assess whether a clinician's prescribing tendency in the previous year predicted current prescribing patterns, after accounting for residents' demographics, comorbidity, functional status and indwelling devices. RESULTS: Among 1695 long-term care physicians, who prescribed for 93 132 residents, there was wide variability in the start of antibiotic treatment (median 45% of patients, interquartile range [IQR] 32%-55%), use of prolonged treatment durations (median 30% of antibiotic prescriptions, IQR 19%-46%) and selection of fluoroquinolones (median 27% of antibiotic prescriptions, IQR 18%-37%). Prescribing tendencies for antibiotics by physicians in 2014 correlated strongly with tendencies in the previous year. After controlling for individual resident characteristics, prior prescribing tendency was a significant predictor of current practice. INTERPRETATION: Physicians prescribing antibiotics exhibited individual, measurable and historical tendencies toward start of antibiotic treatment, use of prolonged treatment duration and class selection. Prescriber audit and feedback may be a promising tool to optimize antibiotic use in long-term care facilities.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".