Merkel Cell Carcinoma
Bibliographic record
Abstract
Merkel cell carcinoma (MCC) is a rare neuroendocrine carcinoma. The cellular origin of MCC may include Merkel cell precursors. The incidence of MCC has increased significantly however trends may have been confounded by evolving diagnostic criteria. The two key aetiologies of MCC are ultraviolet radiation and Merkel cell polyoma virus (MCPyV). Both have unique mechanisms of carcinogenesis. MCC presents non-specifically as a rapidly growing, red-to-violet nodule on sun-exposed areas. Diagnostic accuracy has improved through immunohistochemical markers such as CK-20. Lymph nodes should be evaluated in MCC through examination and sentinel biopsy. USS, CT, MRI and CT-PET may be useful in staging. Management depends on tumour location, stage and comorbidities. MCPyV status may guide treatment strategy in the future. Treatment for the primary MCC is commonly wide local excision followed by radiotherapy, guided by anatomical constraints. There is uncertainty about surgical margins. Treatments for nodal disease have not been determined through trials. They include nodal dissection or radiotherapy for clinically or radiologically apparent disease, and adjuvant nodal irradiation for negative nodes, microscopic disease or following nodal dissection for definite disease. Patients with loco-regional advanced inoperable disease should be considered for combination therapy including chemotherapy, radiotherapy, surgery and immunotherapy. Systemic therapy for advanced disease includes immune checkpoint inhibitors targeting the PD-1/PD-L1 pathway. Avelumab can improve survival in metastatic MCC. Immunotherapy may result in longer disease control. Various other immunotherapeutic and molecular agents are undergoing trials. MCC continues to have a high mortality characterized by high recurrence and early metastases.
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 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) | 0.002 | 0.001 |
| 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".