Consensus Statements for the Use of Administrative Health Data in Rheumatic Disease Research and Surveillance
Bibliographic record
Abstract
OBJECTIVE: Administrative data are increasingly being used for research and surveillance about rheumatic diseases. However, literature reviews have revealed a lack of consistency in methods for conducting observational rheumatic disease studies, a situation that can lead to findings that cannot be compared. Our purpose was to develop best-practice consensus statements about the use of administrative data for rheumatic disease research and surveillance in Canada. METHODS: We convened 52 decision makers, epidemiologists, clinicians, and researchers to a 2-day workshop. Prior to this, participants formed working groups to examine 3 best-practice categories: case definitions, epidemiology methods, and comorbidity and outcomes measurement. The groups conducted systematic or scoping reviews on key topics. At the workshop, evidence from the reviews was presented and consensus-building techniques were used to develop the best-practice statements. The statements were presented, discussed, revised (as needed), and then subjected to voting. RESULTS: Thirteen best-practice consensus statements were developed and endorsed by consensus. For the first category, these consensus statements addressed validation techniques for rheumatic disease case definitions and case ascertainment bias. The consensus statements for epidemiology methods focused on confounding and drug exposure measurement. For comorbidity and outcomes measurement, consensus statements were developed for multiple conditions, including osteoporosis and fragility fractures, cancer, infections, cardiovascular disease, and renal disease. Strengths and limitations of administrative data were identified in relation to each topic. CONCLUSION: Our best-practice consensus statements are consistent with other recent guidelines, including those for rheumatic disease biologics registries, but address additional issues specific to administrative data. Continuing work focuses on disseminating these consensus statements to multiple audiences.
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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 | 0.678 | 0.772 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.019 | 0.027 |
| Research integrity | 0.023 | 0.031 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".