Merkel cell carcinoma of the head and neck: Potential histopathologic predictors
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To identify or confirm any new or suggested independent histopathological predictors in Merkel cell carcinoma (MCC) of the head and neck (HN) correlated with outcome. STUDY DESIGN: Retrospective chart and pathology review. METHODS: Between 1990 and 2010, 58 patients with Merkel cell carcinoma of the head and neck HNMCC were identified for study. Pathologic specimens were reviewed and evaluated for independent prognostic factors and correlated with locoregional recurrence and disease-specific survival. RESULTS: The 2- and 5-year disease-specific survival (DSS) rates were 72.7% and 63.6%, respectively. The local and regional recurrence rates were 12.0% and 24.1%, respectively. A total of 25.9% of the patients developed distant metastases during follow-up. Tumor size (< 1 cm vs. > 1 cm) and the presence of a positive deep resection margin were independently found to be significantly associated with regional recurrence (P = 0.01 and P = 0.04, respectively). No other prognostic factors could be identified. CONCLUSION: Adjuvant radiotherapy cannot remediate a positive resection margin. Given these results, consideration for revision surgery should be considered for a positive deep margin. Frozen section analysis may help to define the margins in this invasive and aggressive disease.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".