Padiatric Asthma in Canada 2016: A cross-sectional study in primary care
Bibliographic record
Abstract
In Canada , 70% of paediatric asthma is managed in primary care. Different collection methods and venues produce a range of values for prevalence and patient demographics. We aimed to (1) describe both of these as seen in primary care in Canada using the national primary care database of the Canadian Primary Care Sentinel Surveillance Network which contains data from 1.8 million patients9 electronic medical records (2) the characteristics of children with asthma in Canada and (3) aspects of the management in primary care. We conducted a cross-sectional study of asthma in children between the ages of 1 and 17 in 2016 and the medications that they were prescribed. We used a validated search algorithm to extract data on children with asthma who had no gaps in the diagnosis greater than 3 years. The prevalence of asthma in this primary care cohort in 2016 was 18.07% (N=44,679). 52% males. Median age was 9 with a mean of 9.4. The median age of onset of the asthma was 7 years (mean 7.1). Prevalence reached a trough at age 12 and peaked at age 4 in both sexes and again in girls at age 17. Boys out numbered girls at all ages up to age 15. Twice as many girls had asthma onset at 16 or at 17 than did boys. Prevalence in the 1-3 year age group was 13%, before 6 was 17% and between 12 and 18 years was 20% Half the children had no recorded prescription for asthma in 2016. 13% had only ever had salbutamol as therapy. 66% had EVER had an ICS (7% in a combination inhaler) 1% had ever had prednisone . Though 26% had seen the physician in the last 12 months, only 4% had a recorded visit for asthma. Despite a high prevalence of childhood asthma , only 1% had severe asthma. Visits were irregular. More structure is needed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".