Childhood wheezing syndromes and healthcare data
Bibliographic record
Abstract
There is convincing evidence that several distinct wheezing syndromes exist in childhood. The purpose of this research was to assess the potential of using healthcare utilization profiles to identify wheezing syndromes in children which are distinct from asthma. Using population-based healthcare administrative data, a cohort of children, aged 5-15 years, with bronchitis diagnoses from time of birth to 1995, but no physician diagnoses of asthma, was followed over the period January 1996-March 1998. In this follow-up period, 13% had subsequent healthcare utilization for asthma, 23% had continued healthcare utilization for bronchitis, and 64% had no further healthcare utilization. The likelihood of bronchitis vs. asthma outcomes was determined for a variety of asthma risk factors. In a cohort of 11,043 children with initial healthcare contact for bronchitis but not asthma, two potentially distinct entities of bronchitis emerged from our data: 1) transient bronchitis, similar to transient wheezing of early childhood, which was associated with winter-only healthcare utilization and absence of allergy, and 2) recurrent bronchitis which differed from asthma on the basis of winter-only healthcare utilization, prematurity at birth, absence of allergy, and low socioeconomic status. Healthcare administrative records can be used to describe the natural history of wheezing in children and to identify markers which may discriminate asthma from other syndromes.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".