Comparison of on-ice and off-ice graded exercise testing in collegiate hockey players
Bibliographic record
Abstract
The purpose of this study was to compare lactate thresholds (LT) and maximal aerobic capacities (VO(2 max) during sport-specific skating (on ice) and cycle ergometry (off ice) in collegiate hockey players. We hypothesized that VO(2 max) and LT would be higher on ice. We also sought to determine if on-ice and off-ice VO(2 max) values were correlated. Twelve collegiate hockey players performed both graded exercise protocols in randomized order to fatigue. Both protocols included 80 s of work during each stage, followed by 40 s of rest to allow for blood lactate sampling. VO(2 max) was significantly higher on ice (46.9 +/- 1.0 mL*kg(-1)*min(-1)) than off ice (43.6 +/- 0.9 mL*kg(-1)*min(-1); p < 0.05). Maximal heart rate (HR(max)) was also higher on ice (192.2 +/- 1.8 beats*min(-1)) than off ice (186.0 +/- 1.5 beats*min(-1); p < 0.01). LT was drastically higher on ice than off ice as a percentage of VO(2 max) (85.9% +/- 1.9% vs. 69.7% +/- 1.3%; p < 0.01) and HR(max) (90.1% +/- 1.3% vs. 79.4% +/- 1.6%; p < 0.01). Finally, no correlation existed between VO(2 max) values off ice and on ice (r = -0.002; p = 0.99). Our results indicate that off-ice VO(2 max) and LT are not adequate predictors of on-ice VO(2 max) and LT in collegiate hockey players. These findings challenge the use of cycle ergometry to assess aerobic capacity at events such as the National Hockey League Entry Draft combine. We suggest that hockey players be tested in a sport-specific manner, regardless of whether those tests are performed on ice or off ice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".