Challenging O2 delivery: metabolism coupling in small muscle mass exercise
Bibliographic record
Abstract
PURPOSE To understand how O 2 del: metabolism matching is defended in the face of increased duration of contraction‐induced mechanical impedance to exercising muscle blood flow. METHODS 7 healthy young males, (21.8 ±1.8 yrs) performed a handgrip ramp exercise protocol (increase contraction force 2.5 kg every 3 min) under Control (CON; 2 s isometric contraction + forearm compression cuff inflation: 4 s relaxation duty cycle) and Impedance (IMP; 2 s contraction + forearm compression + extra 2 s of forearm compression: 2 s relaxation duty cycle). Forearm blood flow ((FBF (ml/min); brachial artery Doppler and Echo ultrasound), mean arterial blood pressure (MAP (mmHg); finger photoplethysmography) were measured. Forearm vascular conductance during relaxation (FVCrelax ml/min/100 mmHg)) was calculated (FBFrelax/MAP x 100). RESULTS Data are mean ± SD. Up to 7.5 kg workload, FBF (therefore O 2 del) was not different between conditions due to compensatory vasodilation in IMP (eg. 7.5 kg FVCrelax IMP 596 ±140 vs. CON 396 ±60, P<0.05). Above 7.5 kg workload a small pressor response in IMP was added (MAP IMP 103.5 ±7.8 vs. CON 98.8 ±8.6, P<0.05), but O 2 del: metabolism matching was not achieved. CONCLUSIONS Compensatory vasodilation successfully maintains O 2 del: metabolism matching at lower workloads. When it fails, a pressor response is evoked, but cannot maintain O 2 del: metabolism matching at higher workloads. 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 | 0.000 | 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) | 0.002 | 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".