Development of the 2023 <scp>ACR/EULAR</scp> Antiphospholipid Syndrome Classification Criteria, Phase III‐D Report: Multicriteria Decision Analysis
Bibliographic record
Abstract
OBJECTIVE: The 2023 American College of Rheumatology/EULAR antiphospholipid syndrome (APS) classification criteria development, which aimed to identify patients with high likelihood of APS for research, employed a four-phase methodology. Phase I and II resulted in 27 proposed candidate criteria, which are organized into laboratory and clinical domains. Here, we summarize the last stage of phase III efforts, employing a consensus-based multicriteria decision analysis (MCDA) to weigh candidate criteria and identify an APS classification threshold score. METHODS: We evaluated 192 unique, international real-world patients referred for "suspected APS" with a wide range of APS manifestations. Using proposed candidate criteria, subcommittee members rank ordered 20 representative patients from highly unlikely to highly likely to have APS. During an in-person meeting, the subcommittee refined definitions and participated in an MCDA exercise to identify relative weights of candidate criteria. Using consensus decisions and pairwise criteria comparisons, 1000Minds software assigned criteria weights, and we rank ordered 192 patients by their additive scores. A consensus-based threshold score for APS classification was set. RESULTS: Premeeting evaluation of 20 representative patients demonstrated variability in APS assessment. MCDA resolved 81 pairwise decisions; relative weights identified domain item hierarchy. After assessing 192 patients by weights and additive scores, the Steering Committee reached consensus that APS classification should require separate clinical and laboratory scores, rather than a single-aggregate score, to ensure high specificity. CONCLUSION: Using MCDA, candidate criteria preliminary weights were determined. Unlike other disease classification systems using a single-aggregate threshold score, separate clinical and laboratory domain thresholds were incorporated into the new APS classification 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 | 0.095 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".