Improving the quality of surgical pathology reports for breast cancer: a centralized audit with feedback.
Bibliographic record
Abstract
CONTEXT: Good communication of pathologic characteristics of a malignancy is crucial to therapy choices and accurate prognostication. The information must be easily retrieved from a surgical pathology report. OBJECTIVES: To evaluate, first in 1999, the quality of surgical pathology reports for segmental breast resections for cancer in Quebec hospitals. Subsequently, to reevaluate, in 2003, the same indicators to determine if the first surveillance, with feedback, was associated with an improvement in the quality of the reports. DESIGN: All Quebec hospitals performing the preset number of 20 or more segmental breast resections for cancer in 1999 and 2003 participated. A committee of pathologists, after review of the literature, chose 7 diagnostic elements deemed vital to a surgical pathology report for conservative breast cancer surgery. Medical archivists in each institution were instructed on how to retrieve the data. The main outcome measure was the presence or absence of the diagnostic information clearly presented on the surgical pathology report. RESULTS: Fifty-one hospitals participated in 1999 and 50 in 2003. Overall, conformity improved from 85.0% in 1999 for the first evaluation to 92.5% in 2003 for the second evaluation (P < .001). Six of the 7 indicators showed an improvement in the level of conformity between the first and second evaluations. Conformity was weakest for recording the distance between the tumor and the resection margin (68.2%) and vascular/lymphatic invasion (61.4%) in 1999. CONCLUSIONS: Surveillance of quality of surgical pathology reports, with feedback, is significantly associated with an improvement in the quality of reports.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".