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 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 | 0.064 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".