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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".