Evolution of inspiratory and expiratory muscle pressures during endurance exercise
Bibliographic record
Abstract
We investigated the relationship between minute ventilation (VE) and net respiratory muscle pressure (Pmus) throughout the breathing cycle [Total Pmus = mean Pmus, I (inspiratory) + mean Pmus, E (expiratory)] in six normal subjects performing constant-work heavy exercise (CWHE, at approximately 80% maximum) to exhaustion on a cycle ergometer. Pmus was calculated as the sum of chest wall pressure (elastic + resistive) and pleural pressure, and all mean Pmus variables were averaged over the total breath duration. Pmus, I was also expressed as a fraction of volume-matched, flow-corrected dynamic capacity of the inspiratory muscles (P(cap, I)). VE increased significantly from 3 min to the end of CWHE and was the result of a significantly linear increase in Total Pmus (Delta = 43 +/- 9% from 3 min to end exercise, P < 0.005) in all subjects (r = 0. 81-0.99). Although mean Pmus, I during inspiratory flow increased significantly (Delta = 35 +/- 10%), postinspiratory Pmus, I fell (Delta = -54 +/- 10%) and postexpiratory expiratory activity was negligible or absent throughout CWHE. There was a greater increase in mean Pmus, E (Delta = 168 +/- 48%), which served to increase VE throughout CWHE. In five of six subjects, there were significant linear relationships between VE and mean Pmus, I (r = 0.50-0.97) and mean Pmus, E (r = 0.82-0.93) during CWHE. The subjects generated a wide range of Pmus, I/P(cap, I) values (25-80%), and mean Pmus, I/P(cap, I) increased significantly (Delta = 42 +/- 16%) and in a linear fashion (r = 0.69-0.99) with VE throughout CWHE. The progressive increase in VE during CWHE is due to 1) a linear increase in Total Pmus, 2) a linear increase in inspiratory muscle load, and 3) a progressive fall in postinspiratory inspiratory activity. We conclude that the relationship between respiratory muscle pressure and VE during exercise is linear and not curvilinear.
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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 | 0.001 | 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".