Electronic Synoptic Operative Reporting: Assessing the Reliability and Completeness of Synoptic Reports for Pancreatic Resection
Bibliographic record
Abstract
BACKGROUND: Electronic synoptic operative reports (E-SORs) have replaced dictated reports at many institutions, but whether E-SORs adequately document the components and findings of an operation has received limited study. This study assessed the reliability and completeness of E-SORs for pancreatic surgery developed at our institution. STUDY DESIGN: An attending surgeon and surgical fellow prospectively and independently completed an E-SOR after each of 112 major pancreatic resections (78 proximal, 29 distal, and 5 central) over a 10-month period (September 2008 to June 2009). Reliability was assessed by calculating the interobserver agreement between attending physician and fellow reports. Completeness was assessed by comparing E-SORs to a case-matched (surgeon and procedure) historical control of dictated reports, using a 39-item checklist developed through an internal and external query of 13 high-volume pancreatic surgeons. RESULTS: Interobserver agreement between attending and fellow was moderate to very good for individual categorical E-SOR items (kappa = 0.65 to 1.00, p < 0.001 for all items). Compared with dictated reports, E-SORs had significantly higher completeness checklist scores (mean 88.8 +/- 5.4 vs 59.6 +/- 9.2 [maximum possible score, 100], p < 0.01) and were available in patients' electronic records in a significantly shorter interval of time (median 0.5 vs 5.8 days from case end, p < 0.01). The mean time taken to complete E-SORs was 4.0 +/- 1.6 minutes per case. CONCLUSIONS: E-SORs for pancreatic surgery are reliable, complete in data collected, and rapidly available, all of which support their clinical implementation. The inherent strengths of E-SORs offer real promise of a new standard for operative reporting and health communication.
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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 | 0.032 | 0.141 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.000 | 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 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".