Agreement between self-report and administrative health data on occurrence of non-cancer chronic disease among participants of the BC generations project
Bibliographic record
Abstract
= 29,736) linked self-reported chronic disease history data to a Chronic Disease Registry (CDR) that applied algorithms to administrative health data to ascertain diagnoses of multiple chronic diseases in the Province of British Columbia. For the 10 diseases captured by both self-report and the CDR, including asthma, chronic obstructive pulmonary disease (COPD), diabetes, hypertension, multiple sclerosis, myocardial infarction, osteoarthritis, osteoporosis, rheumatoid arthritis, and stroke, we calculated Cohen's kappa coefficient to examine concordance of chronic disease status (i.e., ever/never diagnosed) between the data sources. Using CDR data as the gold standard, we also calculated sensitivity, specificity, and positive-predictive value (PPV) for self-reported chronic disease occurrence. The prevalence of each chronic disease was similar across both data sources. Substantial levels of concordance (0.66-0.73) and moderate to high sensitivities (0.64-0.92), specificities (0.98-0.99) and PPVs (0.55-0.84) were observed for diabetes, hypertension, multiple sclerosis, and myocardial infarction. We did observe degree of concordance to vary by age, sex, body mass index (BMI), health perception, and ethnicity across most of the chronic diseases that were evaluated. While administrative health data are imperfect, they are less likely to suffer from bias, making them a reasonable gold standard. Our results demonstrate that for at least some chronic diseases, self-report may be a reasonable method for case ascertainment. However, characteristics of the study population will likely have impacts on the quality of the data.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".