Merkel Cell Carcinoma: Local Recurrence Rate Versus Radiation Dose Study from a 949-Patient Database
Bibliographic record
Abstract
Background: Optimal radiotherapy doses for Merkel cell carcinoma are unknown. Methods: After a PubMed literature search, we analyzed data by Equivalent Dose in 2-Gy fractions (EQD2). Results: 939/949 data were evaluable: 728/939 (77.5%) presented with localized disease, of which 171 were irradiated alone, with a median primary EQD2 of 50 (14.0-72.0) Gy2. Local recurrence (LR) was 23.4% (40/171). The remaining patients were controlled with a median EQD2 of 50 (23.3-72.0) Gy2. Thirteen patients were given definitive radiotherapy EQD2<50 vs >50 Gy2 to gross primaries: LR were 23.1% (3/13) vs 12.5% (1/8)(P=0.0004). Few patients received >60 Gy2. After adjuvant radiotherapy <50 vs >50 Gy2 to 156 primaries, LR were 18.8% (6/32) vs 12.8% (12/124); for <60 vs >60 Gy2, 15.5% (16/103) vs 8.7% (2/23)(P=0.52). LR after <50 Gy2 was 25% (3/12) for positive margins versus 17.4% (4/23) for negative margins; for >50 Gy2: 15% (3/20) versus 4.8% (3/62), respectively (P=0.36). Conclusions: For definitive radiotherapy, EQD2<50 Gy2 demonstrates significant higher LR than >50 Gy2 (P=0.0004). For adjuvant radiotherapy, a trend of higher LR with < 50 Gy2 was seen. Large prospective multicenter studies are required to define the optimal doses for definitive and adjuvant MCC treatment.
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) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".