Heavy-intensity cycling and running work-rate associated to VO2max affects isokinetic strength, the dynamic control ratio but not the conventional H:Q ratio
Bibliographic record
Abstract
BACKGROUND: Bilateral strength asymmetry and fatigue predispose athletes to various injuries and conventional methods appear to be poor predictors of lower extremity muscular performance under NF conditions. OBJECTIVE: The purpose of the study was to compare the conventional Hcon/Qcon (HQR) ratio and the dynamic control ratio (DCR: Hecc/Qcon) under non-fatiguing (NF) and fatiguing (F) conditions and verify the effects of heavy-intensity constant running and cycling exercise on the isokinetic performance. METHODS: Twenty healthy male participants performed running and cycling VO[Formula: see text]max at work-rate associated with the achievement of VO[Formula: see text]max (TTE). Isokinetic muscle strength performance was tested at 60 and 180[Formula: see text]/s before and after these sessions with 48-hour intervals. Quadriceps (QFR) and hamstring (HFR) muscle fatigue rates were also calculated during these sessions. Blood lactate concentration was measured before and two-minutes after running and cycling TTE. RESULTS: No between-condition differences were found for the HQR while the DCR decreased significantly at 180[Formula: see text]/s following cycling and running sessions ([Formula: see text] 0.05). Cycling TTE was positively correlated with in dominant ([Formula: see text] 0.535, [Formula: see text] 0.015) and non-dominant ([Formula: see text] 0.446, [Formula: see text] 0.048) QFR. Positive correlations were also found between running TTE and dominant ([Formula: see text] 0.500, [Formula: see text] 0.25) and non-dominant ([Formula: see text] 0.465, [Formula: see text] 0.039) HFR. CONCLUSIONS: The DCR obtained at fast angular velocities following a strenuous exercise seems to be the best indicator of muscle performance while its assessment under F conditions reveals higher ratios compared to NF conditions and conventional methods.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".