Muscle cellular properties in the ice hockey player: a model for investigating overtraining?
Bibliographic record
Abstract
In this study, we hypothesized that athletes involved in 5-6 months of sprint-type training would display higher levels of proteins and processes involved in muscle energy supply and utilization. Tissue was sampled from the vastus lateralis of 13 elite ice hockey players (peak oxygen consumption = 51.8 ± 1.3 mL·kg(-1)·min(-1); mean ± standard error) at the end of a season (POST) and compared with samples from 8 controls (peak oxygen consumption = 45.5 ± 1.4 mL·kg(-1)·min(-1)) (CON). Compared with CON, higher activities were observed in POST (p < 0.05) only for succinic dehydrogenase (3.32 ± 0.16 mol·(mg protein)(-1)·min(-1) vs. 4.10 ± 0.11 mol·(mg protein)(-1)·min(-1)) and hexokinase (0.73 ± 0.05 mol·(mg protein)(-1)·min(-1) vs. 0.90 ± 0.05mol·(mg protein)(-1)·min(-1)) but not for phosphorylase, phosphofructokinase, and creatine phosphokinase. No differences were found in Na(+),K(+)-ATPase concentration (β(max): 262 ± 36 pmol·(g wet weight)(-1) vs. 275 ± 27 pmol·(g wet weight)(-1)) and the maximal activity of the sarcoplasmic reticulum Ca(2+)-ATPase (98.1 ± 6.1 µmol·(g protein)(-1)·min(-1) vs. 102 ± 3.3 µmol·(g protein)(-1)·min(-1)). Cross-sectional area was lower (p < 0.05) in POST but only for the type IIA fibres (6312 ± 684 μm(2) vs. 5512 ± 335 μm(2)), while the number of capillary counts per fibre and the capillary to fibre area ratio were generally higher (p < 0.05). These findings suggest that elite trained ice hockey players display elevations only in support of glucose-based aerobic metabolism that occur in the absence of alterations in excitation-contraction processes.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".