Outcome of tailored education & retraining: feasibility study of performance & adherence in children with asthma: a RCT (OUTERSPACERS)
Bibliographic record
Abstract
Background: Poor adherence leads to poorer outcomes. Smart Aerochamber Spacers are prototype novel electronic monitoring devices that record inhaler use & technique simultaneously1. Methods: Single centre feasibility trial conducted Mar 23-Feb 25. This 3-month study was open to CYP 6-17yrs with asthma taking controller medication via pMDI+spacer. CYP were randomised 1:1 to standard care or tailored education (TE). Outcomes were inclusion speed, medication adherence, inhaler technique, clinical effects (FEV1, ACQ, FeNO), system usability (SUS) & net promoter score (NPS). Results: 40 CYP took part (20 randomised to TE). 8 withdrew. At enrolment mean age was 9.6yrs (SD 2.1), ppFEV1 93.4% (SD 10.8), median FeNO 31ppb (IQR 19-48). 17/40 CYP had uncontrolled asthma (cACT/ACT score <21). Asthma control & lung function improved nonsignificantly in CYP in the initial 4-week run in period. FeNO fell significantly in 4 weeks to 15ppb (IQR 8-32), p<0.01. Baseline adherence was 58% (21.7%) & mean inhaler performance score was 71% (20.4%). Falls in FeNO were sustained in the TE group (p<0.05). Reported parent satisfaction with the device was high, with high median SUS (95%, IQR 87.5-100) & NPS (+82). CYP reported significantly lower NPS scores (+37) than their parents (p<0.01) Conclusion: Recruitment to a Smart Spacer education trial is feasible & measures of asthma control improved in CYP taking part. Parents reported high satisfaction with the device, with lower CYP ratings. Errors in technique at home are common & amenable to coaching. Larger studies are required to assess clinical effectiveness of this technology. 1. Dierick BJH, et al. Respir Med 2023;218:107376
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".