Does a single bout maximal effort forearm exercise test for determining critical impulse result in maximal oxygen delivery and consumption in men? A randomized crossover trial
Bibliographic record
Abstract
In a single bout maximal effort isometric forearm handgrip exercise test (maximal effort exercise test, MXT), contraction impulse exhibits exponential decay to an asymptote equivalent to critical impulse (CI). It is unknown whether oxygen delivery (O2del) and consumption ([Formula: see text]) achieved at CI are maximal. Healthy men participated in a randomized crossover trial at Queen’s University (Kingston, ON) between October 2017–May 2018. Participants completed an MXT and forearm incremental exercise test to limit of tolerance (IET-LOT) (7 completed MXT followed by IET-LOT vs. 4 completed IET-LOT followed by MXT) within a 2 week period. Data are presented as mean ± standard deviation. Maximal forearm blood flow (FBF) and O2del were not different in 11 men (21 ± 2.5 years) between MXT and IET-LOT (FBF = 473.8 ± 132.2 mL/min vs. 502.3 ± 152.3 mL/min; P = 0.482, ηp2 = 0.015; O2del = 85.2 ± 23.5 mL/min vs. 92.2 ± 37.0 mL/min; P = 0.456, ηp2 = 0.012). However, MXT resulted in greater maximal [Formula: see text] than IET-LOT (44.5 ± 15.2 mL/min > 36.8 ± 11.4 mL/min; P = 0.007, ηp2 = 0.09), due to greater oxygen extraction (54.0 ± 10.0% > 44.4 ± 8.6%; P = 0.021, ηp2 = 0.185). As CI was 88.6 ± 8.2% of IET-LOT contraction impulse, maximal O2 cost of contractions in MXT was greater than IET-LOT (0.45 ± 0.14 mL/min/Ns > 0.33 ± 0.09 mL/min/Ns; P < 0.001, ηp2 = 0.166). In healthy men, MXT identifying CI results in similar peak oxygen delivery but greater peak [Formula: see text] via increased extraction compared to an IET-LOT, indicating increased oxygen cost. MXT-CI may better estimate maximal [Formula: see text] than traditional IET-LOT for this exercise modality.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".