Mohs Micrographic Surgery Dermatopathology Concordance in Canada: A Single-Institution Experience
Bibliographic record
Abstract
BACKGROUND:: Mohs micrographic surgery (MMS) is a surgical modality that achieves high cure rates of nonmelanoma skin cancers but is dependent on accurate histologic examination of surgical margins. Therefore, quality assurance is essential to ongoing assessment of histological margins. OBJECTIVES:: To prospectively determine the concordance rate between a Mohs surgeon (MS) and dermatopathologist (DP) with respect to tumour status (ie, present or absent) and tumour type. Secondary end points were to determine the relationship between discordant interpretations and slide quality and to assess the feasibility of using an electronic webform for data collection. METHODS:: Ten percent (10%) of the planned MMS cases between January 2015 and March 2016 were randomly selected by a histotechnologist at the start of each month. The MS and DP were blinded to the chosen cases, and slides were reviewed independently at the beginning of the following month. Data were collected using an online webform. A blinded third party determined if there were discrepancies in interpretation, and any discordant slides were reviewed together and a consensus was reached. RESULTS:: A total of 270 slides from 54 total cases were reviewed. The overall tumour status concordance rate was 93.6%. Cohen's κ was 0.86. Tumour type concordance was 98.9%. No discrepancy required a change in patient care. All discrepant slides were from cases that required multiple stages. CONCLUSIONS:: This is the first study looking at MS-DP concordance in Canada, and our findings support the MS acting as his or her own pathologist.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".