Educational intervention to reduce treatment of asymptomatic bacteriuria in long-term care
Bibliographic record
Abstract
Objective To determine if an educational intervention can decrease the inappropriate antibiotic treatment of long-term care (LTC) residents with asymptomatic bacteriuria (ASB). Design Prospective chart audit between May and July 2017. Setting Seven LTC facilities in Regina, Saskatchewan, Canada. Participants Chart audits were performed on all LTC residents over 18 years of age with a positive urine culture. Educational sessions and tools were available to all clinical staff at participating LTC facilities. Intervention Fifteen-minute educational sessions were provided to LTC facility staff outlining the harms of unnecessary antibiotic use, antibiotic resistance and the diagnostic criteria of a urinary tract infection (UTI). Educational sessions were complimented with posters and pocket cards that summarised UTI diagnostic criteria. Main outcome measure The primary outcome measure was the number of residents who received inappropriate antibiotic treatment for ASB. Secondary outcome measures included the appropriateness of urine culture tests, number of tests and cost associated with inappropriate treatments. Results In the preintervention period, 172 urine culture and sensitivity (UC&S) tests were performed, 62 (36.0%) were positive and 50/62 (80.6%) residents had ASB based on chart review. In the postintervention period, 151 UC&S tests were performed, 50 (33.1%) were positive and 35/50 (70.0%) residents had ASB. There was a statistically significant decrease in the number of residents treated with antibiotics for ASB, from 45/50 (90%) preintervention to 22/35 (62.9%) postintervention (χ 2 =9.087, p=0.003). Conclusions An educational intervention was associated with a statistically significant decrease in inappropriate antibiotic treatment of LTC residents with ASB.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".