Introduction of Temporal Artery and Large Vessel Ultrasound for the Diagnosis of Giant Cell Arteritis: A Single Canadian Center Experience
Bibliographic record
Abstract
Objectives Diagnostic imaging in giant cell arteritis (GCA) is increasingly being used to replace biopsy (TAB), with the advent of temporal artery and large vessel ultrasound (TA/LV US) showing better sensitivity and comparable specificity to TAB.[1] The purpose of our study is to share and validate our real-world experience since introducing TA/LV US at The Ottawa Hospital. Methods A retrospective chart review was performed on patients referred to RC for query GCA between October 2023-May 2024. We included only suspected new cases of GCA, excluding Takayasu’s arteritis (TAK). Patients were also excluded if they took glucocorticoids for >30 days (known to reduce the sensitivity US). Our TA/LV US protocol measured the intimal medial thickness (IMT) in transverse and longitudinal views of the common/frontal/parietal/facial/subclavian/axillary/carotid arteries. Positive scans were identified based on increased intimal media thickness and presence of halos/slope signs. We extracted patient demographics, prednisone duration, US and TAB results, and final clinical diagnosis in at least 3 months follow-up. The primary outcome measure was the sensitivity and specificity of TA/LV US and TAB taking the clinical diagnosis as the reference standard. Results A total of 170 patients underwent a TA/LV US during this timeframe. Two patients were referred for TAK and of the remaining 168, 77 were excluded given duration of prednisone use (Figure). Of the 91 patients remaining, the median age was 73 (interquartile range [IQR], 60-79) years and 54 (59%) were female. 39/91 (43%) patients were given a clinical diagnosis of GCA. Of these, 35/39 (89.7%) had a positive TA/LV US. Of the 58/91 patients who underwent TAB, 35 (60%) were positive. 11/39 patients diagnosed with GCA with US alone and no TAB. Sensitivity and specificity for TA US was 89.7% and 100% while TAB was 85.7% and 100% respectively. After ≥3 months of follow-up 6/52 (12%) of those not diagnosed with GCA were diagnosed with an alternate ailment (Still’s, lymphoma, skull invasive meningioma, staphylococcus aureus bacteremia, mast cell activation syndrome and a tooth abscess). Conclusion Our brief real-world experience with TA/LV US has demonstrated comparable specificity and marginally better sensitivity for the diagnosis of GCA, although longer term data and follow-up would be required to better understand the role for each modality moving forward. Given emerging use of TA/LV US in the diagnosis of GCA and operator dependency, we highlight the importance of validation of technique in the diagnosis of GCA. [1.] Schmidt WA. Rheumatology 2018;57(Suppl_2):ii22-31.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".