Cost-effectiveness of Enhanced Recovery Versus Conventional Perioperative Management for Colorectal Surgery
Bibliographic record
Abstract
OBJECTIVE: To determine the cost-effectiveness of enhanced recovery pathways (ERPs) versus conventional care for patients undergoing elective colorectal surgery. BACKGROUND: ERPs for colorectal surgery are clinically effective, but their cost-effectiveness is unknown. METHODS: A multi-institutional prospective cohort cost-effectiveness analysis was performed. Adult patients undergoing elective colorectal resection at 2 university-affiliated institutions from October 2012 to October 2013 were enrolled. One center used an ERP, whereas the other did not. Postoperative outcomes were recorded up to 60 days. Total costs were reported in 2013 Canadian dollars. Effectiveness was measured using the SF-6D, a health utility measure validated for postoperative recovery. Uncertainty was expressed using bootstrapped estimates (10,000 repetitions). RESULTS: A total of 180 patients were included (conventional care: n = 95; ERP: n = 95). There were no differences in patient characteristics except for a higher proportion of laparoscopy in the ERP group. Mean length of stay was shorter in the ERP group (6.5 vs 9.8 days; P = 0.017), but there were no differences in complications or readmissions. Patients in the ERP group returned to work quicker and had less caregiver burden. There was no difference in quality of life between the 2 groups. The cost of the ERP program was $153 per patient. Overall societal costs were lower in the ERP group (mean difference = -2985; 95% confidence interval, -5753 to -373). The ERP had a greater than 99% probability of cost-effectiveness. The results were insensitive to a range of assumptions and subgroups. CONCLUSIONS: Enhanced recovery is cost-effective compared with conventional perioperative management for elective colorectal resection.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".