Tumor Detection After Inflammation or Fibrosis on Mohs Levels
Bibliographic record
Abstract
BACKGROUND: In Mohs micrographic surgery (MMS), many surgeons will take an additional level based solely on the presence of inflammation or fibrosis. OBJECTIVE: To determine the frequency with which this occurs and parameters predicting tumor discovery on successive levels. MATERIALS AND METHODS: A retrospective study was performed on 22,419 cases treated with MMS between 1996 and May 2011. The surgeons reviewed their own slides in cases where tumor was detected after a level was taken for inflammation or fibrosis. RESULTS: An additional level was taken for inflammation or fibrosis in 6,233 cases (27.8%), resulting in tumor detection in 121 cases (1.9%). Additional levels were taken for inflammation in 66.6% and fibrosis in 63.0%. Fourteen collision tumors were identified and were preceded by inflammation in 71% of cases. DISCUSSION: Factors that may predict the presence of tumor at subsequent levels include eccentrically placed or shallow first levels failing to completely encompass a previous surgical scar. The presence of dense inflammation may signal an adjacent collision tumor. CONCLUSION: Taking an additional Mohs level when dense inflammation or fibrosis is present may be warranted to ensure complete tumor removal.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 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".