A comprehensive evaluation of online inhaler use techniques for obstructive airway disease
Bibliographic record
Abstract
BACKGROUND: Pulmonary inhaler therapy is a core treatment modality for >600 million individuals affected by obstructive airways disease globally. Poor inhaler technique is associated with reduced disease control and increased health care utilization; however, many patients rely on the internet as a technical resource. This study assesses the content and quality of online resources describing inhaler techniques. METHODS: A Google search was conducted in April 2023 capturing the top 5 search results for 12 common inhaler devices. Websites were compared to product monographs for preparation/first use, inhalational technique, and post-usage/device care. They were also assessed using accepted quality metrics (GQS, DISCERN, JAMA Benchmark scores) and clinically relevant aspects based on the literature and consensus statements. RESULTS: Websites regularly excluded critical steps important for proper inhaler technique. They performed best on information related directly to inhalation technique (average median score 78%), whereas steps related to preparation/first use (58%) or post-usage/device care (50%) were less frequently addressed. Median GQS, DISCERN, and JAMA Benchmark scores were 3 [IQR 3-4], 3 [IQR 2-4], and 1 [IQR 1-3], respectively. Clinically relevant factors were only addressed in about one-fifth of websites with no websites addressing smoking cessation, environmental considerations, or risk factors for poor technique. CONCLUSIONS: This study highlights gaps in online resources describing inhaler technique, particularly related to preparation/first use and post-usage/device care steps. Clinically relevant factors were rarely addressed across websites. Improvements in these areas could lead to enhanced inhaler technique and clinical outcomes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".