Identifying Patients with Physician‐Diagnosed Asthma in Health Administrative Databases
Bibliographic record
Abstract
BACKGROUND: Asthma imposes a heavy and expensive burden on individuals and populations. A population-based surveillance and research program based on health administrative data could measure and study the burden of asthma; however, the validity of a health administrative data diagnosis of asthma must first be confirmed. OBJECTIVE: To evaluate the accuracy of population-based provincial health administrative data in identifying adult patients with asthma for ongoing surveillance and research. METHODS: Patients from randomly selected primary care practices were assigned to four categories according to their previous diagnoses: asthma, chronic obstructive pulmonary disease, related respiratory conditions and nonasthma conditions. In each practice, 10 charts from each category were randomly selected, abstracted, then reviewed by a blinded expert panel who identified them as asthma or nonasthma. These reference standard diagnoses were then linked to the patients' provincial records and compared with health administrative algorithms designed to identify asthma. Analyses were performed using the concepts of diagnostic test evaluation. RESULTS: A total of 518 charts, including 160 from individuals with asthma, were reviewed. The algorithm of two or more ambulatory care visits and/or one or more hospitalization(s) for asthma in two years had a sensitivity of 83.8% (95% CI 77.1% to 89.1%) and a specificity of 76.5% (95% CI 71.8% to 80.8%). CONCLUSION: Definitions of adult asthma using health administrative data are sensitive and specific for identifying adults with asthma. Using these definitions, cohorts of adults with asthma for ongoing population-based surveillance and research can be developed.
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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 | 0.013 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".