Bibliographic record
Abstract
Alan Kaplan, 1– 3 David Price 3, 4 1University of Toronto Department of Family and Community Medicine, Toronto, ON, Canada; 2Family Physician Airways Group of Canada, Edmonton, AB, Canada; 3Observational and Pragmatic Research Institute, Singapore, Singapore; 4Centre of Academic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Aberdeen, UKCorrespondence: Alan KaplanUniversity of Toronto Department of Family and Community Medicine, 7335 Yonge Street, Thornhill, Toronto ON, CanadaTel +1 905 883 1100Fax +1 905-884-1195Email for4kids@gmail.comAbstract: The burden of asthma is particularly notable in adolescents, and is associated with higher rates of prevalence and mortality compared with younger children. One factor contributing to inadequate asthma control in adolescents is poor treatment adherence, with many pediatric studies reporting mean adherence rates of 50% or lower. Identifying the reasons for poor disease control and adherence is essential in order to help improve patient quality of life. In this review, we explore the driving factors behind non-adherence in adolescents with asthma, consider their consequences and suggest possible solutions to ensure better disease control. We examine the impact of appropriate inhaler choice and good inhaler technique on adherence, as well as discuss the importance of selecting the right medication, including the possible role of as-needed inhaled corticosteroids/long-acting β 2-agonists vs short-acting β 2-agonists, for improving outcomes in patients with mild asthma and poor adherence. Effective patient/healthcare practitioner communication also has a significant role to engage and motivate adolescents to take their medication regularly.Keywords: asthma, adherence, adolescents, treatment
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".