A randomised clinical trial of feedback on inhaler adherence and technique in patients with severe uncontrolled asthma
Bibliographic record
Abstract
In severe asthma, poor control could reflect issues of medication adherence or inhaler technique, or that the condition is refractory. This study aimed to determine if an intervention with (bio)feedback on the features of inhaler use would identify refractory asthma and enhance inhaler technique and adherence. Patients with severe uncontrolled asthma were subjected to a stratified-by-site random block design. The intensive education group received repeated training in inhaler use, adherence and disease management. The intervention group received the same intervention, enhanced by (bio)feedback-guided training. The primary outcome was rate of actual inhaler adherence. Secondary outcomes included a pre-defined assessment of clinical outcome. Outcome assessors were blinded to group allocation. Data were analysed on an intention-to-treat and per-protocol basis. The mean rate of adherence during the third month in the (bio)feedback group (n=111) was higher than that in the enhanced education group (intention-to-treat, n=107; 73% versus 63%; 95% CI 2.8%–17.6%; p=0.02). By the end of the study, asthma was either stable or improved in 54 patients (38%); uncontrolled, but poorly adherent in 52 (35%); and uncontrolled, but adherent in 40 (27%). Repeated feedback significantly improved inhaler adherence. After a programme of adherence and inhaler technique assessment, only 40 patients (27%) were refractory and adherent, and might therefore need add-on therapy.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".