A glossary of signs and symptoms of giant cell arteritis
Bibliographic record
Abstract
OBJECTIVES: This study seeks to create consensus-based definitions of signs and symptoms of giant cell arteritis (GCA) for use by health care professionals, primarily in research settings. METHODS: Core definitions of signs and symptoms of GCA were extracted from 11 randomised controlled trials of GCA previously reviewed in a systematic literature review conducted in the context of the development of response criteria for GCA. This information was supplemented by definitions from other sources, such as rheumatology textbooks. A 2-round Delphi was performed within an international task force (32 members from 11 countries). The first round aimed to obtain consensus on the descriptive terms defining each sign or symptom, and round 2 rated the importance of these terms. Based on the Delphi study method, preliminary definitions were developed. In 4 online meetings, results of the Delphi were reviewed, and a consensus was achieved on final definitions. RESULTS: Twenty-nine signs and symptoms of GCA were reviewed. Six signs or symptoms of GCA had previously been defined in the literature. A high level of agreement was reached on the definition of 23 signs and symptoms with the following 12 considered characteristic of GCA: headache, temporal artery abnormalities, scalp tenderness, scalp necrosis, jaw claudication, tongue claudication, tongue necrosis, amaurosis fugax, permanent vision loss, fever, limb claudication, and blood pressure inequality. CONCLUSIONS: A glossary of definitions for 23 signs and symptoms of GCA was developed through a consensus process involving international experts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".