Improving perioperative cancer care: experience with the Enhanced Recovery After Surgery (ERAS) programme
Bibliographic record
Abstract
Aim Surgery plays a pivotal role in the management of the majority of patients with cancer. Surgical cancer care in low-and middle-income countries is negatively impacted by high complication rates and failure to rescue the deteriorating patient. Implementation of the Enhanced Recovery After Surgery (ERAS) programme offers an opportunity to improve care. Methods Over eight years, one public and three private sector South African hospitals implemented the ERAS evidence-based colorectal guidelines tailored for context and led by multidisciplinary teams. Demographic variables, treatment and clinical outcomes were collected using an electronic audit system and analysed using statistical software for data science. Primary outcomes included length of stay and complication rates. The relationship between outcomes and compliance with ERAS guidelines year-by-year was evaluated. Results The study comprised 368 and 325 colorectal cancer patients from public and private sector hospitals, respectively, with an overall length of stay of 6 (interquartile ratio 4,9) and 4 (interquartile ratio 3,7) days, respectively. Complication rates were 39.9% (public sector) and 43.7% (private sector). Overall, ERAS compliance was greater than 70% in both sectors and ERAS compliance was greatest in the pre- and intra-operative phase. An association was seen between increasing compliance and decreased length of stay as well as decreased complication rates. Conclusions A robust colorectal cancer ERAS programme can achieve high compliance, decreased length of stay, and fewer complications in South Africa. This study provides a foundation for a large-scale national strategy for ERAS implementation for perioperative cancer care across all disciplines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".