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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".