Assessment of systemic lupus erythematosus diagnoses within Quebec's health administrative databases
Bibliographic record
Abstract
Background: Systemic lupus erythematosus (SLE) is a chronic, relatively uncommon autoimmune disease that has a relapsing-remitting course, with clinical manifestations in various organ systems (cutaneous, renal, and other).To control disease, immunosuppressive drugs are often required.Health administrative databases are useful for studying SLE because of their wide population coverage, and could potentially be used to study SLE incidence, prevalence, clinical manifestations, and medication use.However, because the diagnoses in these administrative databases are not necessarily clinically confirmed, SLE case ascertainment is a methodological challenge.First, some of the methodological issues were examined in this thesis.Second, clinical manifestations and the association between early antimalarial drug use and future renal manifestations were examined in a cohort of SLE patients.ascertainment was shortened by one-year increments, from fifteen years to three years, prevalence estimates decreased and incidence estimates increased.When SLE cases were ascertained over a 10-year data period, the prevalence (61.8 cases per 100,000) was only 4.1% less than the prevalence estimate generated from the full 15-year data period (65.5/100,000).Using this 10-year period, the SLE incidence estimate in 2001 (6.1/100,000) was only 7.3% higher than the initial 2001 incidence estimate (of 5.7/100,000).However, when the 5-year data period was used instead of the 15-year data period SLE cohort, prevalence decreased by 29.9% to 46.0/100,000 (95% CI: 44.4-47.5),and incidence was falsely inflated by 38.3% to 7.9/100,000 (95% CI: 7.3-8.5). Conclusion:This study showed that varying the algorithm definition and the data period can change incidence and prevalence estimates considerably, so all algorithm features, including the length of time in which the data spans, should be selected carefully and explicitly stated.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".