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Record W3171238906 · doi:10.1016/j.jdin.2021.04.002

Comparison of community pathologists with expert dermatopathologists evaluating Breslow thickness and histopathologic subtype in a large international population-based study of melanoma

2021· article· en· W3171238906 on OpenAlexaffabout
Joseph Michael Yardman‐Frank, Baillie Bronner, Stefano Rosso, Lynn From, Klaus J. Busam, Pam Groben, Paul Tucker, Anne Ε. Cust, Bruce K. Armstrong, Anne Kricker, Loraine D. Marrett, Hoda Anton‐Culver, Stephen B. Gruber, R. P. Gallagher, Roberto Zanetti, Lidia Sacchetto, Terry Dwyer, Alison Venn, Irene Orlow, Peter A. Kanetsky, Li Luo, Nancy E. Thomas, Colin B. Begg, Marianne Berwick, Isidora Autuori, Pampa Roy, Anne S. Reiner, Tawny W. Boyce, Terence Dwyer, Richard P. Gallagher, Joseph D. Bonner, Kathleen Conway, David W. Ollila, Pamela A. Groben, Sharon N. Edmiston, Honglin Hao, Eloise Parrish, Jill S. Frank, David C. Gibbs, Timothy R. Rebbeck, Julia Lee Taylor, S. Madronich

Bibliographic record

VenueJAAD International · 2021
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsBC Cancer AgencyCancer Care Ontario
FundersNational Cancer InstituteNational Health and Medical Research CouncilNational Institutes of HealthMemorial Sloan-Kettering Cancer Center
KeywordsMedicineMelanomaBreslow ThicknessPopulationSkin cancerCancerDermatologyOncologyInternal medicinePathologyBreast cancerCancer research

Abstract

fetched live from OpenAlex

To the Editor: As of 2019 National Cancer Institute data show that melanoma is the fifth most common cancer in the United States.1 There has been a recent push to include the histopathologic subtype of nodular melanoma as an independent prognostic classifier due to the identification of associated aggressive histopathologic characteristics and shorter recurrence-free times.2,3

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)0.008
metaresearch head score (Gemma)0.056
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.056
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.062
GPT teacher head0.384
Teacher spread0.322 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

Explore more

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