Margin status of basal cell carcinoma: What can be done better?
Bibliographic record
Abstract
PURPOSE: Guidelines on clinical margins for basal cell carcinoma (BCC) excisions were recently published, yet the ambiguity regarding the margin continues for surgeons and pathologists. The purpose of this study was to determine the incomplete excision rate of BCC, determine the factors associated with incomplete excision, and evaluate the completeness of reporting between surgeon and pathologist. METHODS: A single-center retrospective analysis was conducted on pathology reports from single excisions of BCC specimens between January 1, 2019 to December 31, 2020. The primary outcome was the incomplete excision rate (positive margins) as reported by pathologist. Logistic regression was used to determine the relationship between incomplete excision rate and anatomical location, pathologist, and surgeon. The completeness of surgeon pathology requisition forms was evaluated qualitatively. RESULTS: Seven hundred and fifty-six pathology reports were included. The incomplete excision rate was 12% (n = 94). The most common site of incomplete excision was head and neck (n = 87, 15%), followed by trunk (n = 5, 7%), and extremities (n = 2, 2%). Five hundred and seventy-nine specimens from 6 surgeons and 9 pathologists were included in the logistic regression analysis. The Wald test showed that the location was significantly associated with incomplete excision (p < 0.05), whereas surgeon and pathologist reports were not (p > 0.05). Regarding missing information, only 47 (6%) pathology reports included "excision" in the requisition form. Four hundred and three (53%) specimens had no clinical history. CONCLUSIONS: The incomplete excision rate found in this study falls within the report range in the literature. Neither surgeon nor pathologist had significant association with incomplete excision. Incomplete excision rate of BCC may be inflated owing to the lack of standardization in requisition form and pathology reporting.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".