Development of a Drug Treatment-Based Severity Measure in Childhood Asthma
Bibliographic record
Abstract
Valid measures of severity are crucial in asthma pharmacoepidemiological research. This study reports the development and validation of a severity measure in childhood asthma for application to health care administrative data. A drug treatment-based asthma severity measure was developed following the stepped care approach to treatment, and this was applied to a cohort of 16,862 children who met a case definition for asthma drug prescription use between January 1995 and March 1996. Assessments were made of the measure's reliability, validity, and responsiveness to change over time. The drug treatment-based asthma severity measure classified 42% of children as having mild asthma, 37% as having moderate asthma, 19% as having moderate-severe asthma, and 2% as having severe asthma. Agreement on severity classification between two successive time periods was excellent (kappa = 0.82). Children classified as having severe asthma were significantly more likely than children with mild-moderate asthma to have previous asthma hospitalizations, to visit asthma specialists, to have high physician utilization, and to require hospital critical care. They were more likely to be reclassified as having severe asthma 2 years later. These findings show that a drug treatment-based severity measure in childhood asthma, which can be applied to prescription data, has good reliability and validity, and is responsive to changes in asthma severity over time.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".