Predictors of Expiratory Flow Limitation during Exercise in Healthy Males and Females
Bibliographic record
Abstract
RATIONALE: It is unclear whether the frequency and mechanisms of expiratory flow limitation (EFL) during exercise differ between males and females. PURPOSE: This study aimed to determine which factors predispose individuals to EFL during exercise and whether these factors differ based on sex. We hypothesized that i) EFL frequency would be similar in males and females and ii) in females, EFL would be associated with indices of low ventilatory capacity, whereas in males, EFL would be associated with indices of high ventilatory demand. METHODS: Data from n = 126 healthy adults (20-45 y, n = 60 males, n = 66 females) with a wide range of cardiorespiratory fitness (81%-182% predicted maximal oxygen uptake) were included in the study. Participants performed spirometry and an incremental cycle exercise test to exhaustion. Standard cardiorespiratory variables were assessed throughout exercise. The tidal flow-volume overlap method was used to assess EFL based on a minimum threshold of 5% overlap between the tidal and the maximum expiratory flow-volume curves. Predictors of EFL during exercise were determined via multiple logistical regression using anthropometric, pulmonary function, and peak exercise data. RESULTS: During exercise, EFL occurred in 49% of participants and was similar between the sexes (females = 45%, males = 53%; P = 0.48). In males, low forced expired flow between 25% and 75% of forced vital capacity and high slope ratio as well as low end-expiratory lung volume, high breathing frequency, and high relative tidal volume at peak exercise were associated with EFL ( P < 0.001; Nagelkerke R2 = 0.73). In females, high slope ratio, high breathing frequency, and tidal volume at peak exercise were associated with EFL ( P < 0.001; Nagelkerke R2 = 0.61). CONCLUSIONS: Despite sex differences in respiratory system morphology, the frequency and the predictors of EFL during exercise do not substantially differ between the sexes.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".