Sensitivity of forearm critical power to acute manipulation of perfusion pressure
Bibliographic record
Abstract
PURPOSE We tested the hypothesis that manipulating perfusion pressure (PP) impacts forearm critical power (fCP). METHODS 9 healthy young (23 ± 2.6 yrs) males completed 10 min fCP tests in each of arm above (A) and below (B) heart level (forearm PP A < B by ~30 mmHg). fCP (average of force impulse in last 30 s of test), forearm blood flow (FBF; echo and Doppler ultrasound), arterial pressure (MAP; finger photoplethysmography), O 2 consumption (VO 2 ; venous blood samples, Fick eqn) were measured during exercise. RESULTS mean ± SD. Responders (all with compromised fCP in A vs. B; 21 ± 7 vs. 30 ± 6 kg·s, p=0.01) and non‐responders (no compromise to fCP in A vs. B; 29 ± 17 vs. 27 ± 16 kg·s, P=0.14) were identified. Responders exhibited O 2 D compromise in A vs. B (164 ± 60 vs. 178 ± 65 ml O 2 /min, p=0.04), and all had lower VO 2 in A vs. B but this was not statistically significant (VO 2 88 ± 30 ml/min vs. 104 ± 40 ml/min, p=0.12). Non‐responders had no compromise to O 2 D in A vs. B (153 ± 26 vs. 164 ± 28 ml O 2 /min, p=0.53), nor any compromise to VO 2 (99 ± 21 ml/min vs. 106 ± 28 ml/min, p=0.58). No clear pattern regarding pressor or vasodilatory compensation to protect O 2 D was found (Responders A vs. B, ΔFVC p=0.01, ΔMAP p=0.12; Non‐responders A vs. B ΔFVC p=0.22, ΔMAP p=0.37). CONCLUSIONS Reductions in perfusion pressure can reduce forearm critical power in individuals who cannot defend O 2 D. These data highlight the importance of O 2 D to fCP. NSERC
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".