Bibliographic record
Abstract
Merkel cell carcinoma has been a focus of active scientific investigation in recent years and new information on the topic has emerged. Although uncommon, this primary cutaneous neuroendocrine carcinoma, usually involving the head/neck of elderly individuals, has a poor prognosis. Within the past two decades, an increase in the incidence of the tumor and the discovery of its link to the Merkel cell polyomavirus have focused medical attention on the lesion. The resulting studies have improved our understanding of the biology of the neoplasm and contributed to clinical care. Specifically, two pathogenic subsets of the tumor have come to light, the majority due to Merkel cell polyomavirus and the minority caused by ultraviolet radiation-induced genetic damage. This dichotomy carries prognostic implications favoring the former subset. In addition, having capitalized on the known susceptibility of the tumor to immune influences, investigators have recently discovered its responsiveness to immune checkpoint inhibition. This revelation has constituted a therapeutic milestone at the clinical level. Herein we provide an overview of the topic, outline updates in the field and place an emphasis on dermatopathologic aspects of Merkel cell carcinoma.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".