Feasibility of a virtual multiple breath washout training program
Bibliographic record
Abstract
To maintain multiple breath washout (MBW) training capacity for clinical trials during the COVID-19 pandemic we developed a virtual alternative to in-person training. Here we report the feasibility of a fully virtual training program for the Exhalyzer® D (EcoMedics AG, CH) MBW device. Mixed media eLearning modules (Articulate) and a live webinar were developed and added to existing training components. Virtual training included 4 eLearning modules, a webinar and knowledge test on device calibration, MBW testing and test quality control. Participants were asked to complete a feedback survey (5-point Likert scale) at the end of training. Trainees then underwent the standard MBW qualification procedure. To date, 111 MBW naïve participants from 57 sites across North America, Europe and Australia have completed virtual training. 50% (56/111) trainees completed the feedback survey, 93% (52/56) gave positive feedback regarding the design and ease of use of the virtual components. 93% (52/56) either agreed or strongly agreed that the modules enhanced their learning experience. However, only 59% (33/56) felt confident in conducting a MBW test after completing the training, which is lower than previously reported for in-person training (88/103; 85%). To date, 30 trainees have attempted qualification with 100% (30/30) success, which is comparable to past in-person training (54/57; 95%) (Au JWY., et al. Pediatr Pulmonol. 2018; 53(S2): 777). Virtual MBW training is feasible and can effectively expand capacity; initial feedback indicates that a combination of virtual and hands-on learning may still be preferred. Enriching virtual resources allows us to reduce in-person requirements and increase flexibility of training. Funded by CFF
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".