Development of The American Society of Colon and Rectal Surgeons’ Rectal Cancer Surgery Checklist
Bibliographic record
Abstract
BACKGROUND: There is excellent evidence that surgical safety checklists contribute to decreased morbidity and mortality. OBJECTIVE: The purpose of this study was to develop a surgical checklist composed of the key phases of care for patients with rectal cancer. DESIGN: A consensus-oriented decision-making model involving iterative input from subject matter experts under the auspices of The American Society of Colon and Rectal Surgeons was designed. SETTINGS: The study was conducted through meetings and discussion to consensus. PATIENTS: Patient data were extracted from an initial literature review. MAIN OUTCOME MEASURES: The checklist was measured by its ability to improve care in complex rectal surgery cases by reducing the possibility of omission through the division of treatment into 3 distinct phases. RESULTS: The process generated a 25-item checklist covering the spectrum of care for patients with rectal cancer who were undergoing surgery. LIMITATIONS: The study was limited by its lack of prospective validation. CONCLUSIONS: The American Society of Colon and Rectal Surgeons rectal cancer surgery checklist is composed of the essential elements of preoperative, intraoperative, and postoperative care that must be addressed during the surgical treatment of patients with rectal cancer.
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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 | 0.107 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".