Pharmacotherapeutic prophylaxis and post-operative outcomes within an Enhanced Recovery After Surgery (ERAS®) program: A randomized retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Pharmacotherapy prophylaxis embedded in Enhanced Recovery After Surgery (ERAS®) protocols is largely unknown because data related to agent choice, dosing, timing, and duration of treatment currently are not collected in the ERAS Interactive Audit System (EIAS®). This exploratory retrospective randomized cohort study characterized pharmacologic regimens pertaining to prophylaxis of surgical site infections (SSI), venous thromboembolism (VTE), and post-operative nausea and vomiting (PONV). MATERIALS AND METHODS: The records of 250 randomly-selected adult patients that underwent elective colorectal (CR) and gynecologic/oncology procedures (GO) at an ERAS® site in North America were abstracted using REDCap. In addition to descriptive statistics, bivariate associations between categorical variables were compared. RESULTS: Rates of SSI, VTE, & PONV were 3.3%, 1.1%, and 53.6%, respectively. Mean length of stay (LOS) for CR was 6.9 days and for GO, 3.5 days (p < 0.001). The most common antibiotic prophylaxis was one-time combination cefazolin 2 g and metronidazole 500 mg between 16 and 30 min preoperatively after chlorhexidine skin preparation. The most frequent VTE prophylaxis was tinzaparin 4500 units SC daily continued for at least 7 days after hospital discharge in oncology patients. PONV was related to longer LOS in both groups. Total morphine milligram equivalents (MME) was positively related to PONV and LOS in both CR & GO groups. CONCLUSION: Guideline-consistent pharmacologic prophylaxis for SSI and VTE for both CR and GO patients was associated with low complication, LOS, and readmission rates. LOS in both groups was highly influenced by total MME, incidence of PONV and multi-modal anesthesia.
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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 | 0.005 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".