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Record W2602746210 · doi:10.1177/2040622317700089

Update on the management of giant cell arteritis

2017· review· en· W2602746210 on OpenAlexaff
Janet Roberts, Alison Clifford

Bibliographic record

VenueTherapeutic Advances in Chronic Disease · 2017
Typereview
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineGiant cell arteritisTocilizumabVasculitisDissection (medical)UstekinumabStroke (engine)Intensive care medicineDiseaseDermatologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

Giant cell arteritis (GCA) is a large vessel vasculitis that may be associated with significant complications such as blindness, stroke, or aortic aneurysm and dissection in a subset of patients. Given the serious side effects associated with prolonged courses of glucocorticoids and frequent relapses experienced when doses are tapered, increased efforts are being dedicated to the discovery of safer and more effective therapies to control this disease. The purpose of this review is to critically evaluate the role of glucocorticoid-sparing agents in the medical management of GCA with a special focus on the most recent evidence regarding the role of biologic agents, including tocilizumab (TCZ), abatacept and ustekinumab, and other novel therapies.

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)0.001
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.052
GPT teacher head0.378
Teacher spread0.326 · 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

Citations19
Published2017
Admission routes1
Has abstractyes

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