Colour Doppler ultrasound and the giant cell arteritis probability score for the diagnosis of giant cell arteritis: a Canadian single-centre experience
Bibliographic record
Abstract
OBJECTIVES: The aim was to compare the accuracy of colour Doppler ultrasonography (CDUS) and temporal artery biopsy (TAB) to establish the final diagnosis of GCA and to determine how the GCA probability score (GCAPS) performs as a risk stratification tool. METHODS: referring hospitals) and GCAPS were compared against the final diagnosis of GCA as determined by a GCA expert; CDUS was also compared with TAB results. RESULTS: Data from 198 patients were included: 60 patients with GCA and 138 patients without GCA. Sixty-two patients had a TAB. Using the final diagnosis by a GCA expert as a reference, the sensitivity, specificity, positive predictive value and negative predictive value were 93.3%, 98.5%, 96.6% and 97.1% for CDUS and 69.2%, 100%, 100% and 81.8% for TAB, respectively. The false-negative rate was 6.7% for CDUS and 30.8% for TAB. False-negative TAB mostly occurred when performed in referring hospitals (57.1%) as opposed to our vasculitis centre (21.1%). With a cut-off at 9.5 points, sensitivity for GCAPS was 98.3% and specificity 74.3%. CONCLUSION: CDUS of the temporal and axillary arteries showed a high sensitivity and specificity and helped to diagnose GCA in patients with negative TAB. We validated that GCAPS is a useful clinical tool, with a score of <9.5 making the diagnosis of GCA improbable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".