Optimizing the use of temporal artery biopsy: A retrospective study
Bibliographic record
Abstract
BACKGROUND: Giant cell arteritis is an inflammatory disease of the large- and medium-sized vessels. It is the most common primary vasculitis, with lifetime incidences of 0.5% and 1% in men and women, respectively. Its diagnosis is based upon clinical criteria, which may include temporal artery biopsy. Expected positivity rates of temporal artery biopsies and patient selection remain controversial topics in the literature. METHODS: A cross-sectional retrospective study of 127 patients referred for temporal artery biopsy with a diagnosis of suspected giant cell arteritis between January 2014 and December 2018 was performed. The primary outcome was the positivity rate. The relationships between positivity rates, symptoms, clinical suspicion, biopsy delay, biopsy length and corticosteroid treatment were also studied. RESULTS: A positivity rate of 23.7% (16.6-32.6%) was shown, along with a significant association between jaw claudication and specimen positivity (odds ratio 8.1, p < 0.05). Moreover, there were significant associations between a high initial clinical suspicion of disease and specimen positivity (p < 0.05), as well as a high initial clinical suspicion of disease and pursuit of corticosteroid treatment following biopsy results, regardless of positivity (p < 0.05). The duration of corticosteroid treatment prior to biopsy was not associated with a change in positivity rate. CONCLUSIONS: The positivity rate of temporal artery biopsy was 23.7%. Treatment of patients with negative temporal artery biopsy was associated with maintenance of corticosteroid treatment when the initial clinical suspicion of arteritis was high. Therefore, temporal artery biopsy may not be necessary for patients with a high initial clinical suspicion of giant cell arteritis.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".