Added diagnostic yield of temporal artery magnetic resonance angiography in the evaluation of giant cell arteritis
Bibliographic record
Abstract
OBJECTIVES: Temporal artery magnetic resonance angiography (TAMRA) is a useful tool to investigate possible diagnoses of GCA. As acquired images also reveal other local structures, they may assist in finding alternative diagnoses when assessing for possible GCA. We sought to assess the utility of TAMRA in identifying other significant abnormalities either associated with a diagnosis of GCA or potentially mimicking a clinical presentation of GCA. METHODS: A retrospective cohort study was undertaken at St Joseph's Healthcare in Hamilton, Ontario, Canada between February 2007 and April 2020 and included patients who underwent TAMRA for a possible diagnosis of GCA. Patient demographics, diagnosis and imaging findings were extracted, and descriptive analysis of findings was performed. RESULTS: We included 340 individuals who underwent TAMRA for assessment of a potential diagnosis of GCA and had clinical information available; there were 126 (37.1%) diagnoses of GCA. Fourteen (4.1%) patients had findings on TAMRA that demonstrated an alternative diagnosis, findings were predominantly in the temporomandibular joint, orbit and meninges. Eighteen (14.3%) patients with GCA had intracranial vascular changes that were demonstrative of intracranial vasculitis; one stroke was attributed to intracranial GCA. CONCLUSIONS: TAMRA has proven utility in diagnosing GCA, and these data suggest that it also has utility in identifying alternative diagnoses to rule out the disease. Intracranial vasculitis was also seen in 14.3% of patients; the clinical impact of these findings is currently poorly understood and requires further study.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".