Assessment of the New 2012 EULAR/ACR Clinical Classification Criteria for Polymyalgia Rheumatica: A Prospective Multicenter Study
Bibliographic record
Abstract
OBJECTIVE: To assess the performance of the new 2012 provisional European League Against Rheumatism (EULAR)/American College of Rheumatology (ACR) polymyalgia rheumatica (PMR) clinical classification criteria in discriminating PMR from other mimicking conditions compared with the previous 5 diagnostic criteria in a multicenter prospective study. METHODS: Patients older than 50 years, presenting with new-onset bilateral shoulder pain with elevated acute-phase reactants (APR), were assessed for the fulfillment of the new and old classification/diagnostic criteria sets for PMR. At the end of the 1-year followup, 133 patients were diagnosed with PMR (expert opinion) and 142 with non-PMR conditions [69 rheumatoid arthritis (RA)]. Discriminating capacity, sensitivity, and specificity of the criteria sets were estimated. RESULTS: Discriminating capacity of the new clinical criteria for PMR from non-PMR conditions and RA as estimated by area under the curve (AUC) were good with AUC of 0.736 and 0.781, respectively. The new criteria had a sensitivity of 89.5% and a specificity of 57.7% when tested against all non-PMR cases. When tested against all RA, seropositive RA, seronegative RA, and non-RA control patients, specificity changed to 66.7%, 100%, 20.7%, and 49.3%, respectively. Except for the Bird criteria, the 4 previous criteria had lower sensitivity and higher specificity (ranging from 83%-93%) compared with the new clinical criteria in discriminating PMR from all other controls. CONCLUSION: The new 2012 EULAR/ACR clinical classification criteria for PMR is highly sensitive; however, its ability to discriminate PMR from other inflammatory/noninflammatory shoulder conditions, especially from seronegative RA, is not adequate. Imaging and other modifications such as cutoff values for APR might increase the specificity of the criteria.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".