Effect of Enhanced Recovery After Surgery compliance on postoperative venous thromboembolism
Bibliographic record
Abstract
BACKGROUND: Implementing Enhanced Recovery After Surgery (ERAS) guidelines has been demonstrated to reduce complications; however, it is unknown if ERAS may influence incidence of postoperative venous thromboembolism, a particularly challenging complication. The objective of this study was to examine the association between ERAS compliance and venous thromboembolism across multiple surgery types. METHODS: This retrospective cohort study included adult patients undergoing one of seven ERAS-guided surgeries between 2017 and 2021 at nine hospitals in Alberta, Canada, that implemented ERAS guidelines. The exposure was overall ERAS compliance (categorized as low, moderate, high) and compliance with each ERAS element. The primary outcome was venous thromboembolism within 30 days of surgery. Secondary outcomes included 30-day hospital readmission, emergency department visits and healthcare costs. RESULTS: Of the 8118 included patients, most had colorectal (52.8%) and gynaecologic (26.1%) surgery. There were 118 (1.5%) patients who experienced a postoperative venous thromboembolism. ERAS compliance was associated with developing a venous thromboembolism; each unit increase in the ERAS compliance score was associated with a 23% decrease in the occurrence of venous thromboembolism. More patients with venous thromboembolism had low (11.0%) or moderate (44.1%) overall ERAS compliance compared with those with no venous thromboembolism (5.6% and 34.5% respectively, P = 0.001). Using logistic regression analysis, the overall ERAS compliance score and American Society of Anesthesiologists class remained significant risk factors for developing a venous thromboembolism. CONCLUSIONS: ERAS compliance was associated with decreased odds of postoperative venous thromboembolism across multiple surgical disciplines, highlighting the importance of improving ERAS compliance to decrease postoperative venous thromboembolism.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".