Synoptic Operative Reporting: Documentation of Quality of Care Data for Rectal Cancer Surgery
Bibliographic record
Abstract
Operative reports can be used to evaluate quality of care indicators in surgical patients. This study evaluated documentation of preoperative and intraoperative quality of care indicators for rectal cancer surgery in synoptic reports and traditional dictated reports. Two surgeons independently reviewed 40 prospectively collected synoptic operative reports from rectal cancer cases and a case-matched historical cohort of 40 dictated reports. Rectal cancer–specific quality measures were scored in both report groups using two separate, previously validated checklists. Synoptic reports had significantly higher overall scores on both checklists 1 (mean adjusted score ± SD 76 ± 4 vs 41 ± 19, P < 0.01) and 2 (54 ± 3 vs 24 ± 11, P < 0.01; maximum score of 100 for both checklists). Synoptic reports scored significantly higher in reporting preoperative and intraoperative care indicators. Data were extracted quickly from synoptic reports (mean 3:46 vs 6:21, minutes:seconds to complete checklists, P < 0.05). Synoptic reports are associated with accurate documentation of quality of care data for rectal cancer surgery. Refining the synoptic templates used will further enhance the collection of quality indicators and reporting in complex oncologic procedures.
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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 | 0.026 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".