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Record W4402031392 · doi:10.58931/cect.2024.3348

Update on Giant Cell Arteritis: Essential Information for Ophthalmologists

2024· article· en· W4402031392 on OpenAlexaff
Laura Donaldson

Bibliographic record

VenueCanadian Eye Care Today · 2024
Typearticle
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsMcMaster University
Fundersnot available
KeywordsGiant cell arteritisMedicineArteritisPathologyVasculitisDisease

Abstract

fetched live from OpenAlex

Giant cell arteritis (GCA) is an important cause of irreversible vision loss in the elderly population. For any physician, making this diagnosis can be difficult due to the highly variable clinical presentation of this large-vessel vasculitis. The 1990 American College of Rheumatology (ACR) classification criteria for GCA are frequently used, however, they were developed to classify GCA patients vs those with other forms of vasculitis and are not true diagnostic criteria. Despite the high predilection of GCA for ocular circulations, the original 1990 criteria did not include any signs or symptoms related to vision. The classification criteria were updated by the ACR and European Alliance of Associations for Rheumatology (EULAR) in 2022 with the significant inclusion of “Sudden vision loss” (Table 1).

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)0.006
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0250.012

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 head0.006
GPT teacher head0.250
Teacher spread0.244 · 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
GenreCommentary

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

Citations0
Published2024
Admission routes1
Has abstractyes

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