Multivariable Models to Predict a Diagnosis of Giant Cell Arteritis: Systematic Review and Metaanalysis
Bibliographic record
Abstract
OBJECTIVE: Multiple models to predict a diagnosis of giant cell arteritis (GCA) have been developed to assist clinicians. We conducted a systematic review and metaanalysis of model variables and model performance. METHODS: We searched PubMed, Embase, and the Cochrane Library from January 1990 to April 2024 for studies that used multivariable models to diagnose GCA. Study characteristics, patient characteristics, method of and criteria for diagnosis, and model details were extracted. A metaanalysis of individual signs and symptoms was performed using generic inverse variance. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess individual model risk of bias. Certainty of the effect estimate for each predictor was assessed using Grading of Recommendations, Assessment, Development and Evaluation (GRADE) framework. RESULTS: /L, positive temporal artery ultrasound, and presence of synovitis (predictive of a non-GCA diagnosis). Other factors classically associated with GCA, including vision loss, symptoms of polymyalgia rheumatica, and headache, were found to be predictive with lower certainty of effect. Models included were predominantly found to be at high risk of bias. CONCLUSION: Predictors of GCA were consistent across models; however, models were of poor methodologic quality. Future models to predict a diagnosis of GCA should be constructed with improved methodologic rigor. (PROSPERO registration: CRD42020186725).
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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 | 0.048 | 0.094 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.043 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".