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Record W2146477878 · doi:10.1177/1066896908319775

Incidence of Melanocytic Lesions of the Conjunctiva in a Review of 10 675 Ophthalmic Specimens

2008· review· en· W2146477878 on OpenAlexaffabout
Gustavo Amorim Novais, Bruno F. Fernandes, Rubens Belfort, Enzo Castiglione, Devinder Cheema, Miguel N. Burnier

Bibliographic record

VenueInternational Journal of Surgical Pathology · 2008
Typereview
Languageen
FieldMedicine
TopicOcular Oncology and Treatments
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsConjunctivaIncidence (geometry)MedicineDermatologyPathologyMathematics

Abstract

fetched live from OpenAlex

During the study period, 10,675 human ophthalmic specimens were received at The Henry C. Witelson Ophthalmic Pathology Laboratory and Registry, McGill University, Montreal, Canada. Of those, 271 were conjunctival lesions (2.5%), with 101 being classified as melanocytic: 50 (49.5%) nevi, 36 (35.6%) primary acquired melanoses, and 15 (14.9%) melanomas. After exclusion of referred cases, 85 lesions were included in the study: 44 (51.7%) nevi, 33 (38.8%) primary acquired melanoses, and 8 (9.4%) melanomas. The most prevalent location was the bulbar conjunctiva. Conjunctival melanomas were most commonly found in an older age group than primary acquired melanosis or nevi. Conjunctival nevi were subdivided into compound (32.9%), subepithelial (16.4%), and junctional (2.3%). Primary acquired melanosis were further classified into primary acquired melanosis with atypia (8.2%) and primary acquired melanosis without atypia (30.5%). Primary acquired melanoses was the predisposing lesion in 75% of the cases of melanoma. In our sample, referral bias could alter the distribution of conjunctival pigmented lesions, with a shift toward the malignant end.

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)0.001
metaresearch head score (Gemma)0.003
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: Review · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.058
GPT teacher head0.399
Teacher spread0.341 · 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
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

Citations40
Published2008
Admission routes2
Has abstractyes

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