Prediction of Maximum Lactate Concentration During an All-Out Anaerobic Test in Elite Ice Hockey Players
Bibliographic record
Abstract
International Journal of Exercise Science 16(4) 1385-1397, 2023. The lack of specific on-ice tests to predict maximum lactate concentration limits the ability of coaches to better track and develop their ice hockey players. Thus, this study aimed to develop an equation for indirectly assessing the maximum lactate concentration produced from an all-out on-ice skating effort in elite adolescent ice hockey players. Twenty elite male ice hockey players participated in this study (age = 15.7 ± 1.0 year). The lactate anaerobic skating test (LAST) consisted of skating back and forth on an 18.2 m course at maximal speed with abrupt stops at each end for a total of 6 shuttles (total distance = 218.2 m; average time = 52.0 ± 2.0 s). The oxygen uptake was measured using a portable metabolic analyzer (Cosmed K4b2) and the maximum post-exercise lactate concentration with a Lactate Pro analyzer. The variables used to estimate lactate concentration were time, heart rate, number of skating strides in the last shuffle (6th) and the skating stride index. The average maximum lactate concentration was 14.4 mmol· L−1, which is expected in elite players. The analysis of explained common variance using T-test (r² = 0.759) and linear regression (r² = 0.863) demonstrates the validity of the model. Additionally, the root mean square error (RMSE = 0.60 mmol· L−1), the mean absolute error (MAE = 0.45mmol· L−1) and the standard error of estimate (SEE = 0.69 mmol· L−1) values further confirm the accuracy of the model. Thus, using simple and easy-to-measure variables (i.e., time and skating stride), coaches will be able to monitor more effectively their players’ progress in an effort to optimize their individual on-ice performance.
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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 | 0.001 | 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".