Outcomes of therapeutic strategies in Merkel cell carcinoma among patients aged 0–30 years
Bibliographic record
Abstract
Merkel cell carcinoma (MCC) in patients aged < 30 years is exceptionally rare, representing < 1% of all cases. Limited data exist regarding optimal therapeutic approaches and outcomes in this age group. Our objective was to analyse clinicopathological patterns and therapeutic outcomes of MCC in patients aged 0-30 years through systematic review of the published literature. We extracted all cases of MCC in patients aged 0-30 years published through October 2024, identifying 44 patients from 14 articles. Data on tumour stage, location, treatment modalities, outcomes and recurrence rates were analysed. The cohort comprised 23 male and 21 female patients with a median age of 14.8 years. Head and neck sites were most common (41%), with oral mucosal involvement in 7% of cases - notably higher than in older adults. Advanced presentation was frequent, with 22% presenting with stage IV disease, compared with 10-16% in older cohorts. Treatment analysis of 24 patients revealed that surgery alone achieved only 33% complete resolution (2 of 6), with a 67% relapse rate. Conversely, surgery with adjuvant radiotherapy achieved 100% complete resolution with no documented recurrences. Chemotherapy alone showed no complete responses. Disease-specific mortality was 8%, significantly lower than the 40% 5-year mortality in older adults. In conclusion, young patients with MCC frequently present with advanced disease but achieve excellent outcomes with multimodal therapy. Surgery alone carries unacceptable relapse rates. Standard adult treatment protocols incorporating wide excision and adjuvant radiotherapy are both feasible and highly effective in patients aged < 30 years, with minimal toxicity and superior locoregional control.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".