Classification criteria in rheumatic diseases: A review of methodologic properties
Bibliographic record
Abstract
OBJECTIVE: To identify classification criteria for the rheumatic diseases and to evaluate their measurement properties and methodologic rigor using current measurement standards. METHODS: We performed a systematic review of published literature and evaluated criteria sets for stated purpose, derivation and validation sample characteristics, methods of criteria generation and reduction, and consideration of validity, and reliability. RESULTS: We identified 47 classification criteria sets encompassing 13 conditions. Approximately 50% of the criteria sets were developed based on expert opinion rather than patient data. Of the 47 criteria sets, control samples were derived from patients with rheumatic disease in 15 (32%) sets, from patients with nonrheumatic diseases in 4 (9%) sets, and from healthy participants in 2 (4%) sets. Where patient data were used, the number of cases ranged from 20-588 and the number of controls from 50-787. In only 1 (2%) criteria set was there a distinct separation between investigators who derived the criteria set and clinicians who provided cases and controls. Authors commented on the need for individual criterion to be reliable in 5 (11%) sets, precise in 5 (11%) sets; authors noted the importance of content validity in 12 (26%) sets, and construct validity in 12 (26%) sets. CONCLUSION: The variation in methodologic rigor used in sample selection affects the validity and reliability of the criteria sets in different clinical and research settings. Despite potential deficiencies in the methods used for some criteria development, the sensitivity and specificity of many criteria sets is moderate to strong.
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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 | 0.179 | 0.375 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.040 | 0.040 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".