STOP Signs: A Population-based Interrupted Time Series Analysis of Antibiotic Duration for Complicated Intraabdominal Infection Before and After the Publication of a Landmark RCT
Bibliographic record
Abstract
OBJECTIVE: To determine if the STOP-IT randomized controlled trial changed antibiotic prescribing in patients with Complicated Intraabdominal Infection (CIAI). SUMMARY OF BACKGROUND DATA: CIAI is common and causes significant morbidity. In May 2015, the STOP-IT randomized controlled trial showed equivalent outcomes between four-day and clinically determined antibiotic duration. METHODS: This was a population-based retrospective cohort study using interrupted time series methods. The STOP-IT publication date was the exposure. Median duration of inpatient antibiotic prescription was the outcome. All adult patients admitted to four hospitals in Calgary, Canada between July 2012 and December 2018 with CIAI who survived at least four days following source control were included. Analysis was stratified by infectious source as appendix or biliary tract (group A) versus other (group B). RESULTS: Among 4384 included patients, clinical and demographic attributes were similar before vs after publication. In Group A, median inpatient antibiotic duration was 3 days and unchanged from the beginning to the end of the study period [adjusted median difference -0.00 days, 95% confidence interval (CI) -0.37 - 0.37 days]. In Group B, antibiotic duration was shorter at the end of the study period (7.87 vs 6.73 days; -1.14 days, CI-2.37 - 0.09 days), however there was no change in trend following publication (-0.03 days, CI -0.16 - 0.09). CONCLUSIONS: For appendiceal or biliary sources of CIAI, antibiotic duration was commensurate with the experimental arm of STOP-IT. For other sources, antibiotic duration was long and did not change in response to trial publication. Additional implementation science is needed to improve antibiotic stewardship.
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 | 0.028 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".