Expiratory flow limitation in highly trained endurance athletes: The role of FEF25–75% and ventilatory capacity during treadmill running
Bibliographic record
Abstract
Background Expiratory flow limitation (EFL) during exercise in highly trained individuals is thought to result from increased ventilatory demands that exceed the capacity of the respiratory system, which does not fully adapt to exercise training. Reduced forced expiratory flow between 25 and 75% of forced vital capacity (FEF 25-75% ), a marker of small airway function, may contribute to EFL by limiting the maximum expiratory flows available during the hyperpnea of exercise. This study investigated whether FEF 25-75% , peak minute ventilation (V̇ E ), and breathing patterns differ between highly trained endurance athletes with and without EFL. Methods Forty highly trained endurance athletes (20 males and 20 females; V̇O 2 max: 59.6±9.2 mL∙kg -1 ∙min -1 ) completed spirometry and a maximal incremental cardiopulmonary treadmill exercise test. EFL was assessed by superimposing tidal flow-volume loops within the maximum flow-volume loop according to end-expiratory lung volume. Results During maximal exercise, 40% of participants ( n = 16: 7 males, 9 females) developed EFL, with no significant sex differences ( P >0.05). Athletes with EFL had significantly lower FEF 25-75% (3.45±0.78 vs. 4.16±0.98 L∙s -1 , P= 0.020, d = 0.802) and a higher ventilatory demand-to-capacity ratio (V̇ E /V̇ Ecap ) (0.86±0.14 vs. 0.66±0.11, P <0.01, d = 1.589) compared to those without EFL. There were no significant differences in absolute tidal volume, breathing frequency or V̇ E between groups ( P >0.05). Conclusion In a homogeneous cohort of highly trained endurance athletes, EFL during maximal treadmill exercise appears to be primarily driven by a reduced capacity to generate expiratory flow, as evidenced by lower FEF 25-75%, rather than differences in ventilatory demand.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".