Impact of a hospital-wide computerised approach to optimise the quality of antimicrobial prescriptions in patients with severe obesity: a quasi-experimental study
Bibliographic record
Abstract
Abstract Background Rates of adherence to available recommendations for dose adjustments in patients with severe obesity are generally low. Hence, antimicrobials are often underdosed in these patients. Antimicrobial stewardship programmes can improve the use of antimicrobials in hospitalised patients. The aim of the study was to analyse the impact of an antimicrobial stewardship programme based on a computerised clinical decision support system for optimal dosing and antimicrobial use in inpatients with severe obesity. Methods This quasi-experimental retrospective study using interrupted time series was conducted in an academic centre in Canada from August 2008 to June 2018. The Antimicrobial Prescription Surveillance System was implemented in August 2010 (intervention 1) and specific rules targeting patients with class III obesity (body mass index ≥ 40 kg/m2) were added in June 2014 (intervention 2). Data were collected from all hospitalised adults receiving antimicrobials which required dose adjustment for severe obesity and were stratified by body mass index. Segmented regression analysis of interrupted time series was used to evaluate the impact of the Antimicrobial Prescription Surveillance System on the proportion of inappropriate days of therapy according to posology and on antimicrobial consumption. Results Overall, 65 205 antimicrobial prescriptions (68% non-obese, 25% class I-II obesity, and 7% class III obesity) were analysed. In patients with class III obesity, the intervention was associated with a decrease in the proportion of inappropriate days of therapy (trend after the first intervention, −0.8% per 2-month period [95% CI −1.1 to −0.5], p
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".