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Record W2793685398 · doi:10.1007/s40487-018-0056-8

Uveal Melanoma: A Review of the Literature

2018· review· en· W2793685398 on OpenAlexaff
Manni Singh, Priya Durairaj, Jensen Yeung

Bibliographic record

VenueOncology and Therapy · 2018
Typereview
Languageen
FieldMedicine
TopicOcular Oncology and Treatments
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMelanomaMedicineEnucleationUveaMetastasisDermatologyDiseaseOcular MelanomaPathologyCancerInternal medicineSurgeryCancer research

Abstract

fetched live from OpenAlex

Melanomas affecting different components of the uvea occur with differing frequencies and clinical presentations. Uveal melanoma is diagnosed via funduscopic exam and ancillary tests. These lesions may present with visual findings or incidental findings on physical exam. Metastasis occurs in approximately half of all patients with primary uveal melanoma. The liver is the most common site of metastasis. Enucleation was at one time considered the definitive local treatment for primary uveal melanoma, but has been largely replaced by other therapeutic procedures that aim to prevent metastasis while preserving vision. Unfortunately, metastasis of uveal melanoma almost always proves to be fatal. The current treatment of metastatic uveal melanoma is limited by the intrinsic resistance of uveal melanoma to conventional systemic therapies. Advancements in molecular biology have resulted in the identification of a number of promising prognostic and therapeutic targets. Early detection and therapy are important factors in disease survival. It is imperative that the treating physician be familiar with the clinical features of uveal melanoma and distinguish it from mimickers in order to ensure effective and timely 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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.007
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.039
GPT teacher head0.391
Teacher spread0.352 · 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 designNot applicable
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

Citations87
Published2018
Admission routes1
Has abstractyes

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