Performance characteristics and predictors of temporal artery ultrasound for the diagnosis of giant cell arteritis in routine clinical practice in a prospective cohort.
Bibliographic record
Abstract
OBJECTIVES: The diagnosis of giant cell arteritis (GCA) is primarily a clinical one. Temporal artery (TA) ultrasound (US) has been proposed as a new diagnostic tool. We aimed to assess the performance characteristics of TA US in routine clinical practice. METHODS: All patients presenting with suspected GCA to our institution are recruited to a prospective registry. Patients who had both a TA US and biopsy (TAB) performed at the time of presentation were included in the current study. The performance characteristics of TA US was compared to physician diagnosis at six months following presentation. Predictive factors for a positive TA US were explored in univariate and multivariable logistic regression analyses. RESULTS: 162 patients were included, 123 (76%) with GCA. Mean (SD) duration of glucocorticoid therapy was 6.6 days (19.4) at the time of TA US. TA US had a sensitivity of 52.8% (95%CI 43.7, 61.9) and specificity of 71.8% (95%CI 54.9, 84.5) for the diagnosis of GCA. Glucocorticoid duration did not significantly impact the results. A sequential strategy of TA US followed by TAB in the case of a negative US had a sensitivity of 78.9% (95%CI 70.1, 85.5) and specificity of 71.8% (95%CI 54.9, 84.5), equivalent to a simultaneous testing strategy. The only factor independently predictive of a positive TA US was male sex (OR 5.53, 95% CI 2.72 to 11.22, p<0.001). CONCLUSIONS: TA US is potentially useful in the diagnosis of GCA; however, interpretation of its results requires knowledge of the performance characteristics in the target population.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".