Merkel Cell Carcinoma: Important Aspects of Diagnosis and Management
Bibliographic record
Abstract
Merkel cell carcinoma (MCC) is a highly aggressive primary neuroendocrine tumor. It is suggested in the literature that postoperative radiotherapy may decrease local recurrence and improve overall survival. The purpose of this retrospective review was to determine our experience and review the literature on this aggressive malignancy. Charts of ten patients with MCC seen between 1985 and 1997 were reviewed to obtain clinicopathological data. Eight patients were male with a mean age of 72 years (range 49-90). The head and neck was the most common site, affecting 50 per cent of patients. All patients had primary excisions with documented negative margins. Pathological size ranged from 10 to 40 mm. Initial pathological diagnosis was lymphoma in three cases requiring immunohistochemistry for cytokeratin and neuron-specific enolase for definitive diagnosis. Lymphatic invasion was noted in three patients but only one of these patients had clinical lymph node involvement. The mean follow-up was 54 months (range 6-114) with an 80 per cent one-year survival and 30 per cent 2-year survival. Postoperative radiotherapy was administered to five patients. Of these three died with evidence of both local and distant recurrence. This small retrospective review highlights important points in the management of MCC including pathological diagnosis and benefits of adjuvant radiation therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".