An inverse relationship between asthma prevalence and medication dispensation trend: a 12-year spatial analysis of electronic health record data in Alberta, Canada
Bibliographic record
Abstract
Asthma is a chronic inflammatory disease of the airways that affects over 300 million people worldwide [1]. As of 2019, 2.95 million Canadians were diagnosed with asthma [2]. While there are standard recommendations and guidelines for the diagnosis and treatment of asthma, patients living with this disease often experience poor health-related quality of life [3] and continue to experience exacerbations [4]. Apart from the increasing air pollution, rapid urbanization, and a growing trend in marijuana and vaping, particularly among the adolescents [5–8], poor patient adherence to asthma medications is a major contributing factor to these outcomes [9–12]. Despite efforts to change this narrative through patient and physician education [13–15], it is well reported that adherence to treatment in asthma is varied, with rates of <50% in children [16] and 30–70% in adults [1, 17]. Footnotes This manuscript has recently been accepted for publication in the ERJ Open Research . It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJOR online. Please open or download the PDF to view this article. Conflict of interest: SM reports personal fees from Synergy Respiratory & Cardiac Care (Canada), Permanyer Inc. (Spain), Elsevier Inc. (USA), Apollo Gleneagles Hospital (India), and Institute of Allergy-Kolkata (India), outside the submitted work. Conflict of interest: AF does not have any conflict of interest to declare. Conflict of interest: MB received grants from Canadian Institute of Health research, Sanofi Genzyme, Astra Zeneca, and GSK; and received payments from Astra Zeneca, GSK, Sanofi Genzyme, Valeo, Covis Pharmaceuticals, and Canadian Thoracic Society, outside the submitted work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".