Systematic review of the effects of patient errors using inhaled delivery systems on clinical outcomes in COPD
Bibliographic record
Abstract
Background Errors using inhaled delivery systems for COPD are common and it is assumed that these lead to worse clinical outcomes. Previous systematic reviews have included patients with both asthma and COPD and much of the evidence related to asthma. More studies in COPD have now been published. Through systematic review, the relationship between errors using inhalers and clinical outcomes in COPD, including the importance of specific errors, was assessed.Methods Electronic databases were searched on 27 October 2023 to identify cohort, case–control or randomised controlled studies, which included patients with COPD, an objective assessment of inhaler errors and data on at least one outcome of interest (forced expiratory volume in 1 s, (FEV1), dyspnoea, health status and exacerbations). Study quality was assessed using the Newcastle and Ottawa scales. A narrative synthesis of the results was performed as there was insufficient detail in the publications to allow quantitative synthesis. There was no funding for the review. Results 19 publications were included (7 cohort and 12 case–control) reporting outcomes on 6487 patients. 15 were considered low quality, and most were confounded by the absence of adherence data. There was weak evidence that lower error rates are associated with better FEV1, symptoms and health status and fewer exacerbations. Only one considered the effects of individual errors and found that only some were related to worse outcomes. Conclusion Evidence about the importance of specific errors using inhalers and outcomes would optimise the education and training of patients with COPD. Prospective studies, including objective monitoring of inhalation technique and adherence, are needed. PROSPERO registration number CRD42023393120.
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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 | 0.021 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".