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Record W2207973362 · doi:10.1016/j.jdcr.2015.09.017

Merkel cell carcinoma in organ transplant recipients: Case reports and review of the literature

2015· article· en· W2207973362 on OpenAlexaff
Giorgia L. Garrett, Hanieh Zargham, Joshua M. Schulman, Fatemeh Jafarian, Siegrid S. Yu, Sarah T. Arron

Bibliographic record

VenueJAAD Case Reports · 2015
Typearticle
Languageen
FieldMedicine
TopicPolyomavirus and related diseases
Canadian institutionsMcGill University
Fundersnot available
KeywordsMerkel cell carcinomaMedicineImmunosuppressionSkin cancerDermatologyIncidence (geometry)Merkel cell polyomavirusCarcinomaMetastasisOrgan transplantationPathologyOncologyCancerInternal medicineTransplantation

Abstract

fetched live from OpenAlex

Merkel cell carcinoma (MCC) is an aggressive tumor of the skin and mucous membranes that typically occurs in elderly white men and in immunosuppressed individuals. The reported incidence of MCC has tripled over the last 20 years in the United States1 from 0.15 to 0.44 cases per 100 000 between 1986 and 2001.2 In light of the poor prognosis and high risk for recurrence, metastasis, and death associated with MCC, this increasing trend is of concern. The risk factors for MCC are similar to those for other skin cancers, mainly ultraviolet light exposure, older age, T-cell immunosuppression, fair skin, and male sex.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0030.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.014
GPT teacher head0.257
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreReview

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

Citations15
Published2015
Admission routes1
Has abstractyes

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