The Accuracy of Administrative Data Diagnoses of Systemic Autoimmune Rheumatic Diseases
Bibliographic record
Abstract
OBJECTIVE: To examine the validity of case definitions for systemic autoimmune rheumatic diseases [SARD; systemic lupus erythematosus (SLE), systemic sclerosis (SSc), myositis, Sjögren's syndrome, vasculitis, and polymyalgia rheumatica] based on administrative data, compared to rheumatology records. METHODS: A list of rheumatic disease diagnoses was generated from population-based administrative billing and hospitalization databases. Subjects who had been seen by an arthritis center rheumatologist were identified, and the medical records reviewed. RESULTS: We found that 844 Nova Scotia residents had a diagnosis of one of the rheumatic diseases of interest, based on administrative data, and had had ≥ 1 rheumatology assessment at a provincial arthritis center. Charts were available on 824 subjects, some of whom had been identified in the administrative database with > 1 diagnosis. Thus a total of 1136 diagnoses were available for verification against clinical records. Of the 824 subjects, 680 (83%) had their administrative database diagnoses confirmed on chart review. The majority of subjects who were "false-positive" for a given rheumatic disease on administrative data had a true diagnosis of a similar rheumatic disease. Most sensitivity estimates for specific administrative data-based case definitions were > 90%, although for SSc, the sensitivity was 80.5%. The specificity estimates were also > 90%, except for SLE, where the specificity was 72.5%. CONCLUSION: Although health administrative data may be a valid resource, there are potential problems regarding the specificity and sensitivity of case definitions, which should be kept in mind for future studies.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".