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Record W2283614092 · doi:10.3747/co.23.2859

Management of Uveal Melanoma: A Consensus-Based Provincial Clinical Practice Guideline

2016· article· en· W2283614092 on OpenAlexafffundvenue
Ezekiel Weis, Thomas G. Salopek, J. Gregory McKinnon, Monica Larocque, Claire Temple‐Oberle, Tina Cheng, John A. McWhae, Ron S. Sloboda, Melissa Shea‐Budgell

Bibliographic record

VenueCurrent Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicOcular Oncology and Treatments
Canadian institutionsAlberta Health ServicesAlberta HealthUniversity of CalgaryUniversity of Alberta
FundersAlberta Health Services
KeywordsMedicineGuidelineMelanomaEnucleationMetastasectomyDiseaseRadiation therapySystemic therapyMEDLINEMetastasisOncologyIntensive care medicineInternal medicineSurgeryCancerPathology

Abstract

fetched live from OpenAlex

INTRODUCTION: Survival in uveal melanoma has remained unchanged since the early 1970s. Because outcomes are highly related to the size of the tumour, timely and accurate diagnosis can increase the chance for cure. METHODS: A consensus-based guideline was developed to inform practitioners. PubMed was searched for publications related to this topic. Reference lists of key publications were hand-searched. The National Guidelines Clearinghouse and individual guideline organizations were searched for relevant guidelines. Consensus discussions by a group of content experts from medical, radiation, and surgical oncology were used to formulate the recommendations. RESULTS: Eighty-four publications, including five existing guidelines, formed the evidence base. SUMMARY: Key recommendations highlight that, for uveal melanoma and its indeterminate melanocytic lesions in the uveal tract, management is complex and requires experienced specialists with training in ophthalmologic oncology. Staging examinations include serum and radiologic investigations. Large lesions are still most often treated with enucleation, and yet radiotherapy is the most common treatment for tumours that qualify. Adjuvant therapy has yet to demonstrate efficacy in reducing the risk of metastasis, and no systemic therapy clearly improves outcomes in metastatic disease. Where available, enrolment in clinical trials is encouraged for patients with metastatic disease. Highly selected patients might benefit from surgical resection of liver metastases.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.919
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.112
GPT teacher head0.488
Teacher spread0.377 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations65
Published2016
Admission routes3
Has abstractyes

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