Multiple breath washout in bronchiectasis clinical trials: is it feasible?
Bibliographic record
Abstract
BACKGROUND: Evaluation of multiple breath washout (MBW) set-up including staff training, certification and central "over-reading" for data quality control is essential to determine the feasibility of MBW in future bronchiectasis studies. AIMS: To assess the outcomes of a MBW training, certification and central over-reading programme. METHODS: MBW training and certification was conducted in European sites collecting lung clearance index (LCI) data in the BronchUK Clinimetrics and/or i-BEST-1 studies. The blended training programme included the use of an eLearning tool and a 1-day face-to-face session. Sites submitted MBW data to trained central over-readers who determined validity and quality. RESULTS: Thirteen training days were delivered to 56 participants from 22 sites. Of 22 sites, 18 (82%) were MBW naïve. Participant knowledge and confidence increased significantly (p<0.001). By the end of the study recruitment, 15 of 22 sites (68%) had completed certification with a mean (range) time since training of 6.2 (3-14) months. In the BronchUK Clinimetrics study, 468 of 589 (79%) tests met the quality criteria following central over-reading, compared with 137 of 236 (58%) tests in the i-BEST-1 study. CONCLUSIONS: LCI is feasible in a bronchiectasis multicentre clinical trial setting; however, consideration of site experience in terms of training as well as assessment of skill drift and the need for re-training may be important to reduce time to certification and optimise data quality. Longer times to certification, a higher percentage of naïve sites and patients with worse lung function may have contributed to the lower success rate in the i-BEST-1 study.
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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 | 0.451 | 0.599 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| 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".