Prolonged Antibiotic Treatment in Long-term Care
Bibliographic record
Abstract
IMPORTANCE: Given that most common bacterial infections can be treated with antibiotic courses of 7 or fewer days, reducing standard antibiotic treatment durations may be an avenue to curtailing antibiotic overuse in long-term care. OBJECTIVES: To describe the variability in the duration of antibiotic treatment courses in long-term care across resident recipients and prescribing physicians and to determine whether this variability is influenced by prescriber preference. DESIGN AND SETTING: Province-wide retrospective analysis of residents of Ontario, Canada, long-term care facilities in 2010. PARTICIPANTS: All adults aged 66 years or older who received an incident treatment course with a systemic antibiotic while residing in an Ontario long-term care facility. MAIN OUTCOME MEASURE: Antibiotic treatment duration was examined across residents and prescribing physicians. The proportion of a physician's treatment courses that exceeded 7 days was used to classify short-, average-, and long-duration prescribers. RESULTS: Of 66 901 long-term care residents from 630 long-term care facilities, 50 061 (77.8%) received an incident antibiotic treatment course (with 51 540 antibiotic courses prescribed). The most commonly selected antibiotic treatment course was 7 days (in 21 136 courses [41.0%]), but 23 124 (44.9%) exceeded 7 days. Among the 699 physicians responsible for 20 or more antibiotic treatment courses, the median (interquartile range) proportion of treatment courses beyond 7 days was 43.5% (26.9%-62.9%) (range, 0%-97.1%). Twenty-one percent of prescribers had a higher-than-expected proportion of prescriptions beyond the 7-day threshold. Patient characteristics were similar across short-, average-, and long-duration prescribers. A mixed logistic model confirmed that prescribers were an important determinant of treatment duration (P < .001), with a relative odds of prolonged prescription of 3.84 for 75th vs 25th percentile prescribers. CONCLUSIONS AND RELEVANCE: Antibiotic treatment courses in long-term care facilities are often prescribed for long durations, and this appears to be influenced by prescriber preference more than patient characteristics. Future trials should evaluate antibiotic stewardship interventions targeting prescriber preferences to systematically shorten average treatment durations to reduce the complications, costs, and resistance associated with antibiotic overuse.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".