Development of the 2023 <scp>ACR</scp> / <scp>EULAR</scp> Antiphospholipid Syndrome Classification Criteria, Phase <scp>III</scp> ‐C Report: Assessment of Patient Scenarios (Derivation Cohort) and Refinement of Definitions
Bibliographic record
Abstract
Objective The 2023 American College of Rheumatology (ACR)/EULAR antiphospholipid syndrome (APS) classification criteria aim to identify patients with a high likelihood of APS for research. Phases I/II of our four‐phase methodologic approach resulted in 27 candidate criteria organized in clinical and laboratory domains. Here, we summarize phase III efforts to reduce and refine criteria using patient scenarios. Methods Using standardized definitions for candidate criteria, the Steering Committee collected antiphospholipid antibody (aPL)–positive cases referred for “suspected APS.” Treating physicians assessed APS case likelihood using a Likert scale. Poisson regression calculated risk ratios (RRs) and 95% confidence intervals (CIs) to quantify the direction and size of the association of candidate criteria with “highly likely” versus “equivocal or unlikely” APS, which guided Steering Committee candidate criteria refinement and organization. Results We collected 314 suspected APS cases (137 [44%] highly likely and 177 [56%] equivocal/unlikely APS). Provoking venous thromboembolism (VTE) or arterial thrombosis (AT) risk factors reduced the size of the association with highly likely APS (RR 4.31 [95% CI 2.11–8.78] to RR 1.56 [95% CI 0.89–2.75] for VTE and RR 3.48 [95% CI 1.91–6.32] to RR 1.64 [95% CI 0.77–3.51] for AT). Persistent lupus anticoagulant, anticardiolipin IgG antibody ≥40 U, and anti–β 2 ‐glycoprotein‐I IgG antibody ≥40 U were positively associated with highly likely APS (all P < 0.05). Eventually, items within eight additive and independent clinical and laboratory domains were refined. Conclusion Referred suspected APS cases provided insight into associations of individual candidate criteria with APS likelihood. RR analyses helped refine items and organize the draft classification system into eight additive and independent clinical and laboratory domains.
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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 | 0.196 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".