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Merkel Cell Carcinoma: Local Recurrence Rate Versus Radiation Dose Study from a 949-Patient Database

2025· preprint· en· W4407171543 on OpenAlexfundno aff
Patricia Tai, Michael Veness, Aoife Jones Thachuthara, Jidong Lian, Avi Assouline, Kurian Joseph

Bibliographic record

VenuePreprints.org · 2025
Typepreprint
Languageen
FieldMedicine
TopicPolyomavirus and related diseases
Canadian institutionsnot available
FundersSaskatchewan Cancer Agency
KeywordsMerkel cell carcinomaMedicineRadiation therapyCarcinomaOncologyRadiation doseInternal medicineDatabaseNuclear medicineComputer science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.101
GPT teacher head0.359
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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