Examining asthma quality of care using a population-based approach
Bibliographic record
Abstract
BACKGROUND: Asthma accounts for considerable burden on health care, but in most cases, asthma can be controlled. Quality-of-care indicators would aid in monitoring asthma management. We describe the quality of asthma care using a set of proposed quality indicators. METHODS: We performed a retrospective cross-sectional study using health databases in Saskatchewan, a Canadian province with a population of about 1 million people. We assessed 6 quality-of-care indicators among people with asthma: admission to hospital because of asthma; poor asthma control (high use of short-acting beta-agonists, admission to hospital because of asthma or death due to asthma); no inhaled corticosteroid use among patients with poor control; at least moderate inhaled corticosteroid use among patients with poor control; high inhaled corticosteroid use and use of another preventer medication among patients with poor control; and any main preventer use among patients with poor control. We calculated crude and adjusted rates with 95% confidence intervals. We tested for differences using the chi2 test for proportions and generalized linear modelling techniques. RESULTS: In 2002/03, there were 24 616 people aged 5-54 years with asthma in Saskatchewan, representing a prevalence of 3.8%. Poor symptom control was observed in 18% of patients with asthma. Among those with poor control, 37% were not dispensed any inhaled corticosteroids, and 40% received potentially inadequate doses. Among those with poor control who were dispensed high doses of inhaled corticosteroids, 26% also used another preventer medication. Hospital admissions because of asthma were highest among those aged 6-9 years and females aged 20-44 years. Males and those in adult age groups (predominantly 20-44 years) had worse quality of care for 4 indicators examined. INTERPRETATION: Suboptimal asthma management would be improved through increased use of inhaled corticosteroids and preventer medications, and reduced reliance on short-acting beta-agonist medications as recommended by consensus guidelines.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".