Identification of Grossing Criteria for Intraoperative Evaluation by Frozen Section of Lung Cancer Resection Margins
Bibliographic record
Abstract
Because of a lack of official guidelines, systematic use of intraoperative frozen section for the evaluation of surgical margins in lung oncology constitutes standard practice in many pathology departments. This costly and time-consuming procedure seems unjustified as reported rates of positive margins remain low. We aimed to evaluate clinicopathologic criteria associated with positive margins and establish evidence-based recommendations regarding the use of frozen sections. This retrospective cohort included 1903 consecutive patients with a lung resection for malignant neoplasm between 2006 and 2015. Clinicopathologic data were retrieved from medical files. Univariate and multivariate analyses were used to identify variables associated with a positive margin. Receiver operating characteristic curves and a probability table of positive margins based on tumor-margin distance were created. Our results were confirmed in a validation cohort of 27 patients with positive margins. The rate of positive margins was 3.8%. A positive margin status changed the surgical management in 48.6% of patients. A short macroscopic tumor-margin distance was associated with a higher risk of positive bronchovascular and parenchymal margins in univariate and multivariate analyses. Selecting a 2.0 cm tumor-margin distance cut-off for performing a frozen section would result in a 55.3% reduction of intraoperative evaluations, with a risk of missing a positive margin of 0.61%. Overall, we showed that systematic use of frozen section for intraoperative evaluation of surgical margins is unnecessary. A better selection of patients with a higher risk of a positive margin can be achieved with tumor-margin distance as a simple gross evaluation parameter.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".