Electronic Synoptic Operative Reporting: Assessing the Reliability and Completeness of Synoptic Reports for Pancreatic Resection
Why this work is in the frame
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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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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 | 0.004 | 0.016 |
| 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 | 0.001 |
| 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 it