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 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 | 0.001 |
| 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) | 0.004 | 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".