Case verification of children with asthma in Ontario
Bibliographic record
Abstract
Asthma is an important chronic childhood illness. A population-based surveillance program could measure the burden of illness, but first, the validity of an administrative diagnosis of asthma must be confirmed. The objective was to evaluate the accuracy of population-based outpatient administrative data in identifying children with asthma for the purpose of on-going asthma surveillance and research. Twenty-one primary care physician (PCP) clinics in Ontario participated. Patients under 18 yr old were categorized into three diagnosis categories according to administrative data diagnosis codes: asthma, asthma-related, and non-asthma. In each PCP clinic, for each diagnosis category, 10 charts were randomly selected for abstraction. A panel of experts (blind to the code) reviewed the abstracted charts and identified them as asthma or non-asthma. The reviewers' diagnosis was considered the gold standard. The accuracy of the administrative data diagnosis coding was analyzed using the concepts of diagnostic test evaluation. Six hundred and thirty patient charts were abstracted and reviewed. Overall agreement between the diagnosis provided by expert chart review and the administrative data diagnosis code was 84.8% (p < 0.001), and was 60.2%, 94.8% and 99.5% for the asthma, asthma-related, and non-asthma categories, respectively. Additionally, the sensitivity and specificity were 91.4% and 82.9%, respectively. Agreement between the administrative data diagnosis code and the PCP chart diagnosis was 99.4% (p < 0.001). An administrative data diagnosis code of asthma is sensitive and specific for identifying asthma. By using the results of this study as a starting point, future research will create a cohort of children with asthma to be used for population-based surveillance and research.
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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 | 0.002 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".