Determinants of Expiratory Flow Limitation in Healthy Women during Exercise
Bibliographic record
Abstract
PURPOSE: Expiratory flow limitation (EFL) can occur in healthy young women during exercise. We questioned whether the occurrence and severity of EFL were related to aerobic fitness or anatomical factors. METHODS: Twenty-two healthy young (<40 yr) women performed a progressive cycle test to exhaustion. The subjects' maximum expiratory flow-volume curve was compiled from several effort-graded vital capacity maneuvers before and after exercise. The maximum expiratory flow-volume curve, along with inspiratory capacity maneuvers, was used to determine lung volumes and expiratory flows and to quantify EFL. To determine relative airway size, we used a ratio sensitive to both airway size and lung volume, called the dysanapsis ratio. The subjects were partitioned into two groups based upon the appearance of >5% EFL. RESULTS: Ten subjects showed EFL during exercise. Forced vital capacities (4.4 ± 0.4 vs 3.7 ± 0.4 L, P < 0.001) and forced expiratory flows for any given lung volume were significantly larger in the non-expiratory flow-limited (NEFL) group. The NEFL group's dysanapsis ratio was significantly larger than that of the EFL group (0.27 ± 0.06 vs 0.21 ± 0.04, respectively, P < 0.05), indicating larger airways in the NEFL group. There was no difference between the NEFL and EFL groups with respect to maximal aerobic capacity (50.8 ± 10.0 vs 46.7 ± 5.9 mL·kg(-1)·min(-1), respectively, P = 0.264). At peak exercise, the NEFL group had a significantly higher end-expiratory lung volume than the EFL group (40.1% ± 4.8% vs 33.7% ± 5.7% FVC, respectively, P < 0.05). CONCLUSIONS: We conclude that EFL in women can largely be explained by anatomical factors that influence the capacity to generate flow and volume during exercise rather than fitness per se.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".