Filling the gaps in SARDs research: collection and linkage of administrative health data and self-reported survey data for a general population-based cohort of individuals with and without diagnoses of systemic autoimmune rheumatic disease (SARDs) from British Columbia, Canada
Bibliographic record
Abstract
PURPOSE: Systemic autoimmune rheumatic diseases (SARDs) are a group of debilitating autoimmune diseases, including systemic lupus erythematosus and related disorders. Assessing the healthcare and economic burden of SARDs has been challenging: while administrative databases can be used to determine healthcare utilisation and costs with minimal selection and recall bias, other health, sociodemographic and economic data have typically been sourced from highly selected, clinic-based cohorts. To address these gaps, we are collecting self-reported survey data from a general population-based cohort of individuals with and without SARDs and linking it to their longitudinal administrative health data. PARTICIPANTS: Using administrative data from the province of British Columbia (BC), Canada, we established a population-based cohort of all BC adults receiving care for SARDs during 1996-2010 (n=20 729) and non-SARD individuals randomly selected from the general population. BC Ministry of Health granted us contact information for 12 000 SARD and non-SARD individuals, who were recruited to complete the surveys by mail or online. FINDINGS TO DATE: Four hundred individuals were initially invited to participate, with 135 (34%) consenting and 127 (94%) submitting the first survey (72% completed online). Sixty-three (49.6%) reported ≥1 SARD diagnosis. The non-SARDs group (n=64) was 92% female with mean age 57.0±11.6 years. The SARDs group (n=63) was 94% female with mean age 56.5±13.1 years. Forty-eight per cent of those with SARDs were current-or-former smokers (mean 10.6±16.2 pack-years), and 33% were overweight or obese (mean body mass index of 24.4±5.3). FUTURE PLANS: Health and productivity data collected from the surveys will be linked to participants' administrative health data from the years 1990-2013, allowing us to determine the healthcare and lost productivity costs of SARDs, and assess the impact of patient-reported variables on utilisation, costs, disability and clinical outcomes. Findings will be disseminated through scientific conferences and peer-reviewed journals.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".