Advances in the diagnosis of giant cell arteritis
Bibliographic record
Abstract
PURPOSE OF REVIEW: To summarize recent advances in the diagnosis of giant cell arteritis (GCA). RECENT FINDINGS: Less common manifestations of GCA include corneal edema, proptosis from lacrimal gland ischemia and sensorineuronal hearing loss. Histology studies have suggested that temporal artery biopsies (TAB) with fixed specimen lengths of 15 mm may be adequate to prevent false negative biopsies. In centers with appropriate radiologic expertise, a European rheumatology consensus guideline has proposed Doppler ultrasound as a first-line confirmatory test for GCA in lieu of temporal artery biopsy. Finding extracranial large vessel disease can help to diagnose GCA. Statistical prediction rules can help risk stratify patients with suspected GCA. Age and platelet level when maintained as continuous variables are the strongest predictors for GCA. SUMMARY: GCA can present with diverse ophthalmic and systemic presentations and expedient recognition of same can avoid diagnostic delay and possible vision loss, among other complications. TAB remains the conventional diagnostic standard test for GCA. The use of statistical prediction models and increased expertise in noninvasive imaging techniques such as ultrasound may decrease reliance on TAB, especially in patients determined to be at low risk for GCA.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
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".