Assessing the repeatability of expiratory flow limitation during incremental exercise in healthy adults
Bibliographic record
Abstract
We sought to determine the repeatability of EFL in healthy adults during incremental cycle exercise. We hypothesized that the repeatability of EFL would be "strong" when assessed as a binary variable (i.e., absent or present) but "poor" when assessed as a continuous variable (i.e., % tidal volume overlap). Thirty-two healthy adults performed spirometry and an incremental cycle exercise test to exhaustion on two occasions. Standard cardiorespiratory variables were measured at rest and throughout exercise, and EFL was assessed by overlaying tidal expiratory flow-volume and maximal expiratory flow-volume curves. The repeatability of EFL was determined using Cohen's κ for binary assessments of EFL and intraclass correlation (ICC) for continuous measures of EFL. During exercise, n = 12 participants (38%) experienced EFL. At peak exercise, the repeatability of EFL was "minimal" (κ = 0.337, p = 0.145) when assessed as a binary variable and "poor" when measured as a continuous variable (ICC = 0.338, p = 0.025). At matched levels of minute ventilation during high-intensity exercise (i.e., >75% of peak oxygen uptake), the repeatability of EFL was "weak" when measured as a binary variable (κ = 0.474, p = 0.001) and "moderate" when measured as a continuous variable (ICC = 0.603, p < 0.001). Our results highlight the day-to-day variability associated with assessing EFL during exercise in healthy adults.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".