Validation of a Case Definition to Define Hypertension Using Administrative Data
Bibliographic record
Abstract
We validated the accuracy of case definitions for hypertension derived from administrative data across time periods (year 2001 versus 2004) and geographic regions using physician charts. Physician charts were randomly selected in rural and urban areas from Alberta and British Columbia, Canada, during years 2001 and 2004. Physician charts were linked with administrative data through unique personal health number. We reviewed charts of approximately 50 randomly selected patients >35 years of age from each clinic within 48 urban and 16 rural family physician clinics to identify physician diagnoses of hypertension during the years 2001 and 2004. The validity indices were estimated for diagnosed hypertension using 3 years of administrative data for the 8 case-definition combinations. Of the 3,362 patient charts reviewed, the prevalence of hypertension ranged from 18.8% to 33.3%, depending on the year and region studied. The administrative data hypertension definition of "2 claims within 2 years or 1 hospitalization" had the highest validity relative to the other definitions evaluated (sensitivity 75%, specificity 94%, positive predictive value 81%, negative predictive value 92%, and kappa 0.71). After adjustment for age, sex, and comorbid conditions, the sensitivities between regions, years, and provinces were not significantly different, but the positive predictive value varied slightly across geographic regions. These results provide evidence that administrative data can be used as a relatively valid source of data to define cases of hypertension for surveillance and research purposes.
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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 | 0.091 | 0.243 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".