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Record W2120414871 · doi:10.1155/2012/176173

Role of Sentinel Lymph Node Biopsy in the Management of Merkel Cell Carcinoma

2012· article· en· W2120414871 on OpenAlexaff
Eric Arruda, Kevin Higgins

Bibliographic record

VenueJournal of Skin Cancer · 2012
Typearticle
Languageen
FieldMedicine
TopicPolyomavirus and related diseases
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsMerkel cell carcinomaMedicineSentinel lymph nodeSentinel nodeRadiation therapyBiopsyDiseaseDermatologyEtiologySkin cancerLymphatic systemOncologySurgeryCarcinomaCancerPathologyInternal medicine

Abstract

fetched live from OpenAlex

Merkel cell carcinoma (MCC) is a rare and typically aggressive form of skin cancer. It most commonly affects the elderly and has a predilection for the sun-exposed skin of the head and neck region. Other etiological factors include immune suppression, organ transplantation, and polyoma virus infection. MCC has a propensity to spread to regional lymphatics with a high locoregional recurrence rate. Since its discovery in 1972, treatment paradigms have shifted, with no consensus on optimal management strategies. Currently, standard of care includes surgical intervention to the primary and locoregional site with adjuvant radiotherapy for high-risk disease. In this paper, we discuss the history, pathology, and epidemiology of this rare disease with a focus on the evidentiary basis of treatment protocols. The use of sentinel lymph node biopsy as a management option will be the focus of this paper.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.012
GPT teacher head0.273
Teacher spread0.261 · 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 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

Citations19
Published2012
Admission routes1
Has abstractyes

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