Determinants of exercise performance in normal men with externally imposed expiratory flow limitation
Bibliographic record
Abstract
To understand how externally applied expiratory flow limitation (EFL) leads to impaired exercise performance and dyspnea, we studied six healthy males during control incremental exercise to exhaustion (C) and with EFL at approximately 1. We measured volume at the mouth (Vm), esophageal, gastric and transdiaphragmatic (Pdi) pressures, maximal exercise power (W(max)) and the difference (Delta) in Borg scale ratings of breathlessness between C and EFL exercise. Optoelectronic plethysmography measured chest wall and lung volume (VL). From Campbell diagrams, we measured alveolar (PA) and expiratory muscle (Pmus) pressures, and from Pdi and abdominal motion, an index of diaphragmatic power (W(di)). Four subjects hyperinflated and two did not. EFL limited performance equally to 65% W(max) with Borg = 9-10 in both. At EFL W(max), inspiratory time (TI) was 0.66s +/- 0.08, expiratory time (TE) 2.12 +/- 0.26 s, Pmus approximately 40 cmH2O and DeltaVL-DeltaVm = 488.7 +/- 74.1 ml. From PA and VL, we calculated compressed gas volume (VC) = 163.0 +/- 4.6 ml. The difference, DeltaVL-DeltaVm-VC (estimated blood volume shift) was 326 ml +/- 66 or 7.2 ml/cmH2O PA. The high Pmus and long TE mimicked a Valsalva maneuver from which the short TI did not allow recovery. Multiple stepwise linear regression revealed that the difference between C and EFL Pmus accounted for 70.3% of the variance in DeltaBorg. DeltaW(di) added 12.5%. We conclude that high expiratory pressures cause severe dyspnea and the possibility of adverse circulatory events, both of which would impair exercise performance.
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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) | 0.001 | 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) | 0.001 | 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".