090. A CLINICAL SCORING SYSTEM FOR RISK STRATIFICATION OF GIANT CELL ARTERITIS
Bibliographic record
Abstract
Background: To examine the utility of a clinical scoring system for the risk stratification of patients with suspected GCA in accurately predicting diagnosis. Methods: From a prospective database, we conducted retrospective chart reviews of patients with suspected GCA. Patients were assessed by a rheumatologist and categorized as low, moderate or high risk (pre-test clinical impression). Given a normal scalp artery on MRI has been associated with negative temporal artery biopsy, the majority of low/moderate risk patients underwent MRI first and received a biopsy if MRI results were positive or inconclusive. The Bhavsar-Khalidi (BK) score was developed based on clinical experience and a literature review. Included in the BK score are the following criteria and their respective weightings: typical headache (2), atypical headache (1), scalp tenderness (1), jaw claudication (3), sudden visual loss (4), other visual symptoms (1), polymyalgia symptoms (1), constitutional symptoms (1), temporal artery tenderness (2), temporal artery decreased pulse (2), ESR >40 or CRP >10 (2). A BK score ≤4=low, 5- 7=moderate, and ≥8=high. A BK score was calculated for each patient and compared with clinician’s final diagnosis. Agreement between BK score and physician pre-test clinical impression was determined, as was sensitivity and specificity of physician pre-test clinical impression compared to final GCA diagnosis. Results: Included in the analyses were 135 patients (92 female, mean age 69 years). 122 patients underwent magnetic resonance angiography (MRA) imaging, and 45 had a biopsy. The kappa statistic between BK score and clinician pre-test clinical impression was 0.77 (95% CI = 0.59-0.96). Patients who were classified as moderate or high-risk BK score with a final diagnosis of GCA were considered as accurately diagnosed patients. Using this definition, the BK score sensitivity was 87%, and specificity was 65%. Sensitivity of physician pre-test was 48% and specificity was 94%. Conclusions: The BK scoring tool may be useful for physicians to classify risk of GCA, particularly for non-rheumatologists. With its high sensitivity (87%) and negative predictive value (89%), physicians can apply this tool to safely rule out most low risk patients. Thus moderate and high-risk patients can then appropriately be referred to a rheumatologist, and considered for steroid therapy in the interim. Further research will validate the BK score in a larger population and across different centres, and also explore the tool’s utility in primary care. Disclosures: None
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".