Development, Sensibility, and Validity of a Systemic Autoimmune Rheumatic Disease Case Ascertainment Tool
Bibliographic record
Abstract
OBJECTIVE: Case ascertainment through self-report is a convenient but often inaccurate method to collect information. The purposes of this study were to develop, assess the sensibility, and validate a tool to identify cases of systemic autoimmune rheumatic diseases (SARD) in the outpatient setting. METHODS: The SARD tool was administered to subjects sampled from specialty clinics. Determinants of sensibility - comprehensibility, feasibility, validity, and acceptability - were evaluated using a numeric rating scale from 1-7. Comprehensibility was evaluated using the Flesch Reading Ease and the Flesch-Kincaid Grade Level. Self-reported diagnoses were validated against medical records using Cohen's κ statistic. RESULTS: There were 141 participants [systemic lupus erythematosus (SLE), systemic sclerosis (SSc), rheumatoid arthritis, Sjögren syndrome (SS), inflammatory myositis (polymyositis/dermatomyositis; PM/DM), and controls] who completed the questionnaire. The Flesch Reading Ease score was 77.1 and the Flesch-Kincaid Grade Level was 4.4. Respondents endorsed (mean ± SD) comprehensibility (6.12 ± 0.92), feasibility (5.94 ± 0.81), validity (5.35 ± 1.10), and acceptability (3.10 ± 2.03). The SARD tool had a sensitivity of 0.91 (95% CI 0.88-0.94) and a specificity of 0.99 (95% CI 0.96-1.00). The agreement between the SARD tool and medical record was κ = 0.82 (95% CI 0.77-0.88). Subgroup analysis by SARD found κ coefficients for SLE to be κ = 0.88 (95% CI 0.79-0.97), SSc κ = 1.0 (95% CI 1.0-1.0), PM/DM κ = 0.72 (95% CI 0.49-0.95), and SS κ = 0.85 (95% CI 0.71-0.99). The screening questions had sensitivity ranging from 0.96 to 1.0 and specificity ranging from 0.88 to 1.0. CONCLUSION: This SARD case ascertainment tool has demonstrable sensibility and validity. The use of both screening and confirmatory questions confers added accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".