A Controlled Quasi-Experimental Study of an Educational Intervention to Reduce the Unnecessary Use of Antimicrobials For Asymptomatic Bacteriuria
Bibliographic record
Abstract
BACKGROUND: Asymptomatic bacteriuria (ABU) should only be treated in cases of pregnancy or in-patients undergoing urologic procedures; however, unnecessary treatment of ABU is common in clinical practice. OBJECTIVE: To identify risk factors for unnecessary treatment and to assess the impact of an educational intervention focused on these risk factors on treatment of ABU. DESIGN: Quasi-experimental study with a control group. SETTING: Two tertiary teaching adult care hospitals. PARTICIPANTS: Consecutive patients with positive urine cultures between January 30th and April 17th, 2012 (baseline) and January 30th and April 30th, 2013 (intervention). INTERVENTION: In January 2013, a multifaceted educational intervention based on risk factors identified during the baseline period was provided to medical residents (monthly) on one clinical teaching unit (CTU) at one hospital site, with the CTU of the other hospital serving as the control. RESULTS: During the baseline period, 160/341 (46.9%) positive urine cultures were obtained from asymptomatic patients at the two hospitals, and 94/160 (58.8%) were inappropriately treated with antibiotics. Risk factors for inappropriate use included: female gender (OR 2.1, 95% CI 1.1-4.3), absence of a catheter (OR 2.5, 1.2-5), bacteriuria versus candiduria (OR 10.6, 3.8-29.4), pyuria (OR 2.0, 1.1-3.8), and positive nitrites (OR 2.2, 1.1-4.5). In 2013, only 2/24 (8%) of ABU patients were inappropriately treated on the intervention CTU as compared to 14/29 (52%) on the control CTU (OR 0.10; 95% CI 0.02-0.49). A reduction was also observed as compared to baseline on the intervention CTU (OR 0.1, 0.02-0.7) with no significant change noted on the control CTU (OR 0.47, 0.13-1.7). CONCLUSIONS: A multifaceted educational intervention geared towards medical residents with a focus on identified risk factors for inappropriate management of ABU was effective in reducing unnecessary antibiotic use.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".