The use of multiple respiratory inhalers requiring different inhalation techniques has an adverse effect on COPD outcomes
Bibliographic record
Abstract
BACKGROUND: Patients with COPD may be prescribed multiple inhalers as part of their treatment regimen, which require different inhalation techniques. Previous literature has shown that the effectiveness of inhaled treatment can be adversely affected by incorrect inhaler technique. Prescribing a range of device types could worsen this problem, leading to poorer outcomes in COPD patients, but the impact is not yet known. AIMS: To compare clinical outcomes of COPD patients who use devices requiring similar inhalation technique with those who use devices with mixed techniques. METHODS: A matched cohort design was used, with 2 years of data from the Optimum Patient Care Research Database. Matching variables were established from a baseline year of follow-up data, and two cohorts were formed: a "similar-devices cohort" and a "mixed-devices cohort". COPD-related events were recorded during an outcome year of follow-up. The primary outcome measure was an incidence rate ratio (IRR) comparing the rate of exacerbations between study cohorts. A secondary outcome compared average daily use of short-acting beta agonist (SABA). RESULTS: The final study sample contained 8,225 patients in each cohort (mean age 67 [SD, 10], 57% males, 37% current smokers). Patients in the similar-devices cohort had a lower rate of exacerbations compared with those in the mixed-devices cohort (adjusted IRR 0.82, 95% confidence interval [CI] 0.80-0.84) and were less likely to be in a higher-dose SABA group (adjusted proportional odds ratio 0.54, 95% CI 0.51-0.57). CONCLUSION: COPD patients who were prescribed one or more additional inhaler devices requiring similar inhalation techniques to their previous device(s) showed better outcomes than those who were prescribed devices requiring different techniques.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".