Antibiotic Self-stewardship: Trainee-Led Structured Antibiotic Time-outs to Improve Antimicrobial Use
Bibliographic record
Abstract
BACKGROUND: Antibiotic use is an important quality improvement target. Nearly 50% of antibiotic use is unnecessary or inappropriate. To combat overuse, the Centers for Disease Control and Prevention (CDC) proposed "time-outs" to reevaluate antibiotics. OBJECTIVE: To optimize antibiotic use through trainee-led time-outs. DESIGN: Before-after study. SETTING: Internal medicine (2 units, 46 beds) at a university hospital. PATIENTS: Inpatients (n = 679). INTERVENTION: From January 2012 until June 2013, while receiving monthly education on antimicrobial stewardship, resident physicians adjusted patients' antibiotic therapy through twice-weekly time-out audits using a structured electronic checklist. MEASUREMENTS: Antibiotic costs were standardized and compared in the year before and after the audits. Use was measured as World Health Organization defined daily doses (DDDs) per 1000 patient-days. Total antibiotic use and the use of moxifloxacin, carbapenems, antipseudomonal penicillins, and vancomycin were compared by using interrupted time series. Rates of nosocomial Clostridium difficile infection were compared by using incidence rate ratios. RESULTS: Total costs in the units decreased from $149,743CAD (January 2011 to January 2012) to $80,319 (January 2012 to January 2013), for a savings of $69,424 (46% reduction). Of the savings, $54,150 (78%) was related to carbapenems and $15,274 (22%) was due to other antibiotic classes. Adherence with the auditing process was 80%. In the time-series analyses, the only reliable and statistically significant change was a reduction in the rate of moxifloxicin use, by -1.9 DDDs per 1000 patient-days per month (95% CI, -3.8 to -0.02; P = 0.048). Rates of C. difficile infection decreased from 24.2 to 19.6 per 10,000 patient-days (incidence rate ratio, 0.8 [CI, 0.5 to 1.3]). LIMITATION: Other temporal factors may confound the findings. CONCLUSIONS: An antibiotic self-stewardship bundle to implement the CDC's suggested time-outs seems to have reduced overall costs and targeted antibiotic use. PRIMARY FUNDING SOURCE: None.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".