A Regional Survey on Merkel Cell Carcinoma: A Plea for Uniform Patient Journey Modeling and Diagnostic–Therapeutic Pathway
Bibliographic record
Abstract
Merkel cell carcinoma (MCC) is a rare and aggressive cutaneous neuroendocrine cancer that usually affects the elderly and immunosuppressed in sun-exposed areas. Due to its rarity, it is frequently unrecognized, and its management is not standardized across medical centers, despite the more recent availability of immunotherapy, with avelumab as first-line treatment improving the prognosis even in advanced stages of disease. We conducted a purpose-designed survey of a selected sample of physicians working in the Lazio region, in Italy, to assess their awareness and knowledge of MCC as well as their perspective on assisted diagnostic and therapeutic pathways. The Lazio region, and in particular Rome, is one of the most important academic and non- academic center in Italy dedicated to the diagnosis and treatment of skin cancer. A total of 368 doctors (including 100 general practitioners, 72 oncologists, 87 dermatologists, 59 surgeons, and 50 anatomopathologists) agreed to be part of this survey. Surgeons, oncologists, and dermatologists thought themselves significantly more updated on MCC than primary care physicians, but more than half of the interviewees are interested in CCM training courses and training with clearer and more standardized care pathways. Significant differences have been reported from survey participants in terms of multidisciplinary team set up for MCC management. The identification of specialized centers and the improvement of communication pathways among different specialties, as well as between patients and physicians, could be very beneficial in improving patients' journey modeling and starting a uniform diagnostic and therapeutic pathway for MCC patients in the new era of immunotherapies.
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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".