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Record W3041182352 · doi:10.1111/vop.12796

Golden retriever pigmentary uveitis: Challenges of diagnosis and treatment

2020· review· en· W3041182352 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueVeterinary Ophthalmology · 2020
Typereview
Languageen
FieldMedicine
TopicOcular Diseases and Behçet’s Syndrome
Canadian institutionsnot available
FundersAmerican Kennel Club Canine Health Foundation
KeywordsUveitisMedicineCertificationLabrador RetrieverOptometryOphthalmologyVeterinary medicineSurgeryPolitical scienceLaw

Abstract

fetched live from OpenAlex

Pigmentary uveitis (PU), also known as Golden Retriever Pigmentary Uveitis (GRPU), is a common ocular condition of Golden Retrievers that has severe, vision-threatening ocular complications and can require surgical intervention. In order to ensure consistency in the diagnosis of GRPU between examiners, a specified set of diagnostic criteria must be applied. This is critical to ensure owners, breeders, and veterinary ophthalmologists maintain confidence in the ocular certification process. Therefore, current and former members of the American College of Veterinary Ophthalmologists' Genetics Committee came together to draft this Viewpoint Article on the challenges of diagnosis and treatment of Golden Retriever Pigmentary Uveitis for veterinary ophthalmologists, Golden Retriever owners, and Golden Retriever breeders.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.945
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
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)0.0010.000

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 head0.161
GPT teacher head0.379
Teacher spread0.218 · 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