Synoptic operative reports enhance documentation of best practices for rectal cancer
Bibliographic record
Abstract
BACKGROUND: Implementation of best practices surgical checklists improves patient safety and outcomes. However, documenting performance of these practices can be challenging. The American Society of Colon and Rectal Surgeons developed a Best Practices for Rectal Cancer Checklist (RCC) to standardize and improve the quality of rectal cancer surgery. This study compared the degree to which synoptic (SR) and narrative (NR) operative reports document RCC items. METHODS: Two reviewers independently reviewed a cohort of prospectively collected SR for rectal cancer surgery and a case-matched historical cohort of NR. Reports were reviewed for documentation of performance of operative items on the RCC. Abstraction time and inter-rater agreement were also measured. RESULTS: SR scored significantly higher than NR on the overall checklist score (mean adjusted score ± standard deviation 12.4 ± 0.9 vs. 5.7 ± 1.9, maximum possible score 18, P < 0.001). Reviewers abstracted data significantly faster from SR. Inter-rater agreement between reviewers was high for both types of reports. CONCLUSIONS: SR were associated with reliable and more complete and reliable documentation of items on the RCC. Use of an SR system standardizes operative reporting, providing the opportunity to enhance checklist compliance, and enable timely feedback to improve surgical outcomes for rectal cancer patients.
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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 | 0.064 | 0.358 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".