Self-reported Diagnosis of Rheumatoid Arthritis or Ankylosing Spondylitis Has Low Accuracy: Data from the Nord-Trøndelag Health Study
Bibliographic record
Abstract
OBJECTIVE: Self-reported diagnoses of inflammatory arthritis are not accurate. The primary study aim was to ascertain self-reported diagnoses of rheumatoid arthritis (RA) and ankylosing spondylitis (AS) in the Norwegian population-based Nord-Trøndelag Health Study (HUNT) using hospital case files. The secondary aim was to provide updated estimates of the prevalence and incidence of RA and AS. METHODS: All inhabitants ≥ 20 years old from the county of Nord-Trøndelag were invited. Data from 70,805 unique participants from HUNT2 (1995-1997) and HUNT3 (2006-2008) were included. For participants who self-reported RA or AS, case files from all 3 hospitals in the catchment area were evaluated using standardized diagnostic criteria. RESULTS: Of 2703 self-reported cases of RA, 19.1% were verified in hospital files. Of 1064 self-reported cases of AS, 15.8% were verified. Of 259 cases self-reporting both RA and AS, 8.1% had RA and 5.4% had AS. Overall, a self-report of 1 or both diagnoses could not be verified in 82.1%, including 22.8% with insufficient information or no case file. The prevalence of RA was 768 (95% CI 705-835) per 100,000. The incidence of RA from HUNT2 to HUNT3 was 0.48 (0.41-0.56) per 1000 per year. The prevalence of AS was 264 (228-305) per 100,000. The incidence of AS from HUNT2 to HUNT3 was 0.19 (0.15-0.24) per 1000 per year. CONCLUSION: Self-reported diagnoses of RA and AS are often false-positive. The prevalence and incidence of RA were comparable to reports from similar populations. The incidence of AS was higher than previously reported in a mixed population from Norway.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".