Are En Face Frozen Sections Accurate for Diagnosing Margin Status in Melanocytic Lesions?
Bibliographic record
Abstract
To assess the diagnostic accuracy of margin evaluation of melanocytic lesions using en face frozen sections compared with standard paraffin-embedded sections, we studied 2 sets of lesions in which en face frozen sections were used for analysis of surgical margins (13 from malignant melanomas [MMs] and 10 from nonmelanocytic lesions [NMLs]). Routine permanent sections were cut after routine processing. The slides were mixed and coded randomly. Fifteen dermatopathologists examined the cases separately. Margin status was categorized as positive, negative, or indeterminate. Kappa statistics were calculated per dermatopathologist and per case. One case from each group was excluded because epidermis was not available in the routine sections. Of 330 evaluations (22 cases, 15 dermatopathologists), there were 132 diagnostic discrepancies (40.0%): 66 each for MM and NML (mean per case for both diagnoses, 6). In 9 instances (6.8%), the change was from positive (frozen) to negative (permanent) and in 43 (32.6%), from negative (frozen) to positive (permanent). There was poor agreement between frozen and permanent sections (κ range per dermatopathologist, –0.1282 to 0.6615). If permanent histology is considered the “gold standard” for histologic evaluation, en face frozen sections are not suitable for accurate surgical margin assessment of melanocytic lesions.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".