Use of PFT (spirometry) to diagnose and manage asthma in Ontario, Canada, 2000-2016
Bibliographic record
Abstract
Background: Many national guidelines recommend conducting pulmonary function testing (PFT) for diagnosing and managing asthma. Aim: To describe and quantify PFT using spirometry trends in Ontario, Canada. Methods: A case of asthma is defined as an individual who has at least one asthma hospitalization record or two asthma physician claims over two consecutive years (Gershon AS, et al. Canadian Respiratory Journal. 2009;16(6):183–8; To T, et al. Pediatric Allergy and Immunology. 2006;17(1):69–76). All individuals living in Ontario with asthma were identified using this definition. The population-based cohort from 2000-2016 was linked across various health administrative databases to retrieve data on the use of PFT, hospitalizations, and emergency department (ED) visits. Results: PFT rates range from 44.9 in 2000 to 56 in 2016 per 100 asthma incidence for diagnosing asthma and 10.3 to 6.5 per 100 asthma prevalence for managing asthma. Over 17 years, PFT increased by 25% for diagnosing asthma and decreased by 37% for managing asthma. Asthma hospitalization rates range from 2.31 in 2000 to 0.47 in 2016 per 100 asthma prevalence while ED visit rates range from 4.33 to 1.33 per 100 asthma prevalence. Hence asthma hospitalizations and ED visits decreased by 80% and 70% over time, respectively. Conclusions: Despite guideline recommendations, PFT to diagnose and manage asthma remains low from 2000-2016 in Ontario. Although there are decreasing trends in hospitalizations and ED visits, it is important to promote the use of PFT to objectively diagnose and manage asthma in order to further reduce acute care and improve the health status of people with asthma.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".