Agreement among Six Methods of Predicting the Anaerobic Lactate Threshold in Elite Cross-Country Skiers
Bibliographic record
Abstract
The anaerobic lactate threshold (LTan) is used to prescribe training intensity and measure endurance capacity. The LTan identifies a critical point where small increases in workload result in large increases in blood lactate concentration. LTan is usually predicted through visual inspection of a blood lactate (bLa) vs workload plot. Numerous other methods for predicting LTan exist, and the literature lacks a consensus regarding validity of prediction methods. The purpose of this study was to assess the agreement among visual inspection (VI), maximum distance (Dmax) and modified maximum distance (Dmod) from the lactate curve, Baldari & Guidetti (BG), Dickhuth & Heck (DH) and Keul (K) methods for predicting the LTan. Blood lactate data was gathered from 8 male elite cross country skiers across two treadmill running incremental exercise tests. The above methods were used to predict LTan. Bland-Altman limits of agreement and Lin's Concordance Correlation Coefficient analyses were used to compare methods. Agreement was defined as 95% limits of agreement falling within a maximum allowed difference of ± 0.5 mM bLa between methods. No agreement was found among any of the prediction methods. Mean LTan calculated with the Dmax method was significantly different (p < 0.05) from mean LTan calculated using each other method. We conclude that the six methods for predicting LTan used in this study are not in agreement and should not be considered equivalent for exercise testing purposes. Future studies should compare agreement between LTan methods and the maximal lactate steady state to determine the most valid LTan prediction method.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".