Analysis of the factors influencing the proximity and agreement between critical power and maximal lactate steady state: a systematic review and meta-analyses
Bibliographic record
Abstract
Identifying the boundary between heavy and severe exercise domains is crucial since it demarcates the transition from sustainable to unsustainable exercise. This systematic review aimed to determine differences and agreement between two indices used to determine this boundary, namely critical power (CP) and maximal lactate steady state (MLSS), and how moderators may affect these differences. Ten out of 782 studies found were included in the meta analyses. Random effect meta-analyses were performed to evaluate the mean differences (MD) between CP and MLSS, and moderators’ effect on MD was assessed using meta-regression. CP and MLSS agreement was tested using Bland-Altman meta-analyses on the limits of agreements (LoA) of the MD. Power output (PO) at CP was higher (MD (95% LoA) = 12.42 [−19.23; 44.08] W, p = 0.005) than PO at MLSS, with no differences between CP and MLSS in terms of oxygen uptake (MD (95% LoA) = 0.09 [−0.34; 0.52] L⋅min −1 , p = 0.097), heart rate (MD (95% LoA) = 0.61 [−15.84; 17.05] bpm, p = 0.784), and blood lactate concentration (MD (95% LoA) = 1.63 [−2.85; 6.11] mM, p = 0.240). Intensities at CP ( p = 0.002) and MLSS ( p = 0.010) influenced the MD expressed in W. In conclusion, solely when expressed in PO, CP was higher than MLSS, with larger differences in fitter and younger individuals, emphasizing the possible effect of the indicators used for assessing exercise intensity. Finally, the high interindividual variability observed in the differences between CP and MLSS could compromise their interchangeability in predicting the heavy to severe boundary regardless of the parameter used to assess exercise intensity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".