Optimizing discharge decision‐making in colorectal surgery: a prospective cohort study of discharge practices in a recently implemented enhanced recovery pathway
Bibliographic record
Abstract
AIM: The objectives of this project were (1) to compare time to readiness for discharge by set criteria and actual length of stay (LOS) in a newly implemented colorectal enhanced recovery pathway and (2) to identify reasons for delayed hospital discharge. METHOD: We conducted a prospective cohort study of 73 adult patients (age 67 ± 14 years, 56% men, 51% laparoscopic, 13% stoma creation) undergoing elective colorectal surgery in a university hospital with a recently implemented recovery pathway (<2 years). Time to readiness for discharge (oral intake, flatus, pain control, ability to walk, and no complications) was compared to actual LOS using a correlation-adjusted log-rank test. The treating team was interviewed, and thematic analysis was used to identify reasons for patients remaining in hospital after discharge criteria (DC) were achieved. RESULTS: Median LOS was 6 (4-8) days and median time to readiness for discharge was 5 (3-8) days (P < 0.001). Twenty-eight patients (37%) remained in hospital after DC were achieved. Although some delayed discharges were medically justified (e.g., workup [13%] or treatment of complications not captured by DC [2.6%]), unnecessary hospital stays were common (e.g., perceived need for observation [16%], or patients not willing to be discharged [11%]). CONCLUSIONS: Unnecessary hospital stays were common within a recently implemented enhanced recovery pathway and represent a target for quality improvement. Efforts should be directed at optimizing patient education regarding discharge expectations, early consultation of the discharge planning team and improving discharge decision-making using standardized DC.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".