Contextualizing temporal arterial magnetic resonance angiography in the diagnosis of giant cell arteritis: a retrospective cohort study
Bibliographic record
Abstract
OBJECTIVES: Imaging modalities have become common in evaluating patients for a possible diagnosis of GCA. This study seeks to contextualize how temporal arterial magnetic resonance angiography (TA-MRA) can be used in facilitating the diagnosis of GCA. METHODS: A retrospective cohort study was performed on patients who had been previously referred to a rheumatologist for evaluation of possible GCA in Hamilton, Ontario, Canada. Data including clinical features, inflammatory markers, imaging, and biopsy results were extracted. Multivariable logistic regression model to predict the diagnosis of GCA. Using these models, the utility of TA-MRA in series with or in parallel to clinical evaluation was demonstrated across the cohort as well as in subgroups defined by biopsy and imaging status. RESULTS: In total 268 patients had complete data. Those diagnosed with biopsy- and/or imaging-positive GCA were more likely to demonstrate classic features including jaw claudication and vision loss. Clinical multivariable modelling allowed for fair discriminability [receiver operating characteristic (ROC) 0.759, 95% CI: 0.703, 0.815] for diagnosing GCA; there was excellent discriminability in facilitating the diagnosis of biopsy-positive GCA (ROC 0.949, 0.898-1.000). When used in those with a pre-test probability of 50% or higher, TA-MRA had a positive predictive value of 93.0%; in those with a pre-test probability of 25% or less TA-MRA had a negative predictive value of 89.5%. CONCLUSION: In those with high disease probability, TA-MRA can effectively rule in disease (and replace temporal artery biopsy). In those with low to medium probability, TA-MRA can help rule out the disease, but this continues to be a challenging diagnostic 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".