Medication adherence and risk of severe exacerbations in asthma- A systematic review
Bibliographic record
Abstract
Asthma is a chronic inflammatory disease with a high prevalence worldwide. Asthma therapy includes drugs for asthma control and for quick relief. Especially for controller therapy, adherence is crucial to reduce the risk of exacerbations. Objective: To systematically review the association between adherence to asthma controller therapy and risk of severe exacerbations in children and adults. Methods: Systematic literature search of the PUBMED, EMBASE and Web of Knowledge databases from inception until 2013. Studies were included if there were empirical data about adherence, exacerbations and their association. Quality was assessed using a modified version of the Newcastle Ottawa Scale. Results: The search yielded 2956 unique publications. 33 articles met the inclusion criteria and underwent data extraction and quality scoring. 24 studies used objective measures and 9 used subjective measures to assess adherence. Included studies were published between 1993-2013. Sample sizes ranged from 24 to 97,743 individuals. High levels of heterogeneity across studies in adherence and exacerbation measurements, designs and strategies for evaluating the association, precluded a formal meta-analysis. Although effect measures varied widely and were inconsistent, in high quality studies better adherence tended to be associated with fewer exacerbations. Conclusions: Based on current literature, it is difficult to make strong statements on adherence to asthma therapy and risk of exacerbations. New, well designed prospective studies, using standardized definitions for adherence and treatment outcomes are needed. This is essential to disentangle their association, and to ultimately increase patient adherence.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".