Association Between Different Non-Invasively Derived Thresholds with Lactate Threshold during Graded Incremental Exercise
Bibliographic record
Abstract
International Journal of Exercise Science 11(4): 391-403, 2018. We compared lactate threshold (TLac)with non-invasive markers of an aerobic-anaerobic transition; namely, ventilatory (VT) and tissue saturation index (TSIT) thresholds. While identification of a breakpoint in blood lactate concentration ([BLa]) is common for determination of an aerobic-anaerobic transition, non-invasive measures, VT and NIRS, have also received attention as a means of determining this critical exercise intensity. We hypothesised that one or other of these non-invasive measures would have a strong association with TLac. Thirty-one (n=31) competitive male athletes (mean ± SD, age 29±9 yr, height 1.81±0.1 m, body mass 77.7±10.0 kg) performed graded incremental cycling to volitional exhaustion. Heart rate, TSI and gas exchange data were measured throughout and [BLa] was determined at fixed intervals. Threshold detection involved a segmented linear regression analysis minimising the squared sum of the residuals to determine TLac, TSIT and VT. Workload and HR at TLac, VT and TSIT were analysed using repeated measures ANOVA and correlation assessed using Pearson’s and interclass correlation coefficients. Thresholds at TSIT and TLac were not significantly different (255±35 vs. 249±30 W, P>0.05), suggesting that limitations in O2 delivery could be closely linked to an aerobic-anaerobic transition. However, poor correlation (r=0.55, ICC=0.54 and 95%LoA of +67 and -54 W) suggested other factors may exert an influence. Mean VT occurred at a significantly higher workload than TLac (271 ±35 vs 249±30 W, P<0.001). Consequently, VT proved less useful, giving an indication of when an aerobic-anaerobic transition had already occurred. In conclusion, non-invasive markers of the aerobic transition are not concurrent with TLac.
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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 | 0.003 |
| 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 | 0.001 | 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".