The reproducibility and sample size requirements of exercise-induced bronchoconstriction measurements
Bibliographic record
Abstract
Dry air exercise challenges are frequently used to screen medications that have potential utility in the management of exercise-induced bronchoconstriction (EIB). The purpose of this study was to determine the reproducibility of three outcome measurements made using such challenges, and sample size requirements for drug evaluation studies based on these outcomes. Forty adult subjects with asthma, who tested positively on a screening exercise challenge, were subjected to two further identical challenges, separated by 1 to >35 days. Outcome measurements included the maximum per cent fall in forced expiratory volume in one second (FEV1), after exercise (% fallmax), and the area under the per cent fall in FEV1/time curve for 30 min (AUC30) and 60 min (AUC60) after exercise. The reproducibility of these outcomes, as assessed by intraclass correlation coefficients was 0.72, 0.53 and 0.35 for % fallmax, AUC30 and AUC60 measurements, respectively. The sample size requirements to demonstrate an attenuation of EIB equivalent to a 50% reduction in % fallmax was 9, 14 and 19 subjects for the % fallmax, AUC30 and AUC60 responses, respectively (90% power). It is concluded that the maximum percentage fall in forced expiratory volume in one second has greater reproducibility and results in greater power in clinical trials than area under the curve measurements. Sample size calculation curves are provided which may be used in study design and interpretation of published studies.
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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 | 0.236 | 0.425 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".