Impact of Implementing a Grossing Tumor-margin Distance Threshold for Frozen Section in Oncologic Lung Surgery
Bibliographic record
Abstract
Intraoperative frozen section (FS) examination of oncologic surgical specimens is frequently performed to ensure complete surgical resection. Data on the gross evaluation of surgical margins are limited. We recently published a study suggesting the use of a macroscopic 2.0 cm tumor-margin cutoff during intraoperative evaluation to decrease the number of unnecessary FS. This study aimed to validate the safety and the clinical impacts of implementing a 2.0 cm tumor-margin threshold for FS diagnosis in evaluating surgical margins during oncologic lung surgery. This retrospective analysis included patients who underwent lung resection for primary or metastatic neoplasms between 2018 and 2022 at the Institut Universitaire de Cardiologie et de Pneumologie de Québec, following the implementation of this practice. Clinicopathological data were retrieved from the medical files. Univariate and multivariate analyses were used to identify the variables associated with positive margins. This study included 1575 tumors in 1299 patients. FS evaluations were performed in 24.4% of patients. No positive margins were observed when the tumor-margin distance was >2.0 cm. The incidence rate of positive margins was 2.95%, with parenchymal margins being the most affected. Multivariate analysis identified the tumor-margin distance as a significant predictor of positive margin status. This practice led to a 79.9% reduction in FS evaluations without compromising the margin assessment accuracy or patient safety. A 2.0 cm tumor-margin distance threshold for intraoperative FS evaluation in oncologic lung surgery is safe and effective in reducing unnecessary FS evaluations while maintaining accurate margin assessments.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 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".