Identifying Children with Persistent Asthma from Health Care Administrative Records
Bibliographic record
Abstract
BACKGROUND: Investigation into the origins of asthma is contingent on definitions of asthma, which can differentiate asthma from transient wheezing syndromes in children. OBJECTIVES: This research was undertaken to develop a definition for asthma derived from health care administrative records, which would identify children with persistent asthma. PATIENTS AND METHODS: Using population-based, health care administrative data, children with possible asthma were identified as having one or more physician visits or hospitalizations for asthma or bronchitis diagnoses from January 1995 to December 1995, or, in the absence of asthma-like diagnoses, one or more prescriptions for asthma prophylaxis drugs or ketotifen concomitant with a beta-agonist, or two or more prescriptions for beta-agonists. RESULTS: The likelihood of persistent asthma, defined as repeated health care and prescription use for asthma from 1996 to 1998, was assessed for various asthma markers and risk factors in 29,198 children with possible asthma. Children with asthma prescription drugs or asthma health care use not limited to the winter season were three to six times more likely than children without these characteristics to have persistent asthma. The likelihood of persistent asthma was elevated to a substantial degree in the presence of both of these markers. CONCLUSIONS: The inclusion of these measures in a diagnosis-based definition improves the ability to identify persistent asthma in children.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 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".