Completeness of dictated operative reports in breast cancer—the case for synoptic reporting
Bibliographic record
Abstract
BACKGROUND: Currently, the dictated operative report forms the cornerstone of documenting breast cancer surgery. Synoptic electronic reporting using a standardized template has been proposed for breast cancer operative notes to improve documentation. The goal of this study was to determine the current completeness of dictated operative reports for breast cancer surgery. METHODS: An iterative, consensus-based approach to determining elements of a proposed synoptic surgical operative report identified critical elements. We then evaluated the dictated operative reports of 100 consecutive breast cancer patients for completeness of these elements. RESULTS: Details regarding presentation and diagnosis were frequently incomplete (84%). Among patients undergoing mastectomy, the potential for breast conservation was partially described in only 60%. Only 41% had data regarding intra-operative margin assessment during breast conservation surgery. In axillary lymph node dissections, 92% of patients had complete data about preservation of nerves, yet only 14% of reports contained complete information regarding sentinel lymph node biopsy. Closure was partially described in 91%. CONCLUSIONS: The dictated operative report for breast cancer surgery does not adequately capture important data. A synoptic reporting system, which requires documentation of important elements, is a potentially beneficial tool in breast cancer surgery.
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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 | 0.205 | 0.501 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".