Intraoperative frozen section analysis of margin status as a quality indicator in gastric cancer surgery
Bibliographic record
Abstract
INTRODUCTION: Positive pathologic margins following gastric cancer (GC) resection carries a poor prognosis. We evaluated intraoperative frozen section (IFS) analysis of resection margins (RMs) as a quality indicator in GC surgery. METHODS: Patients referred to a provincial cancer agency with surgically resected non-metastatic GC between 2004 and 2012 were included. Associations between IFS analysis, other baseline characteristics, RMs, and overall survival (OS) were assessed using logistic regression, Kaplan-Meier analyses, and Cox proportional hazards modeling. RESULTS: Among 377 patients, median age was 67 years, 68% were male, and 16% had +RMs. Thirty-four percent of patients underwent IFS analysis, which protected against +RMs (odds ratio [OR]: 0.34, 95% confidence interval [CI]: 0.16-0.73, p = 0.006) and improved OS (hazards ratio [HR]: 0.72, 95% CI: 0.54-0.98, p = 0.037). OS following re-resection of IFS positive patients was similar to IFS negative patients (69 vs. 54 months, p = 0.317). Stage III disease (OR: 12.8, 95% CI: 3.00-55.0, p = 0.001) and gastroesophageal junction tumors (OR: 2.25, 95% CI: 1.05-4.78, p = 0.036) predicted +RMs. Stage III disease led to worse OS (HR: 2.89, 95% CI: 1.92-4.34, p < 0.001) while intestinal histology improved OS (HR: 0.67, 95% CI: 0.50-0.90, p = 0.007). CONCLUSIONS: IFS analysis reduce +RMs and improve OS and should be incorporated in curative intent GC surgery for patients with locally advanced GC.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".