Notice bibliographique
Résumé
Dear Editor-in-Chief, In their letter, Marwood et al. conclude that critical power (CP) rather than maximal lactate steady-state (MLSS) offers the best representation of the maximal metabolic steady-state (i.e., critical intensity). While defending this position, the authors claim: That we previously proposed (1) “CP…overestimates the heavy–severe boundary.” This is a distortion of our views. Although overestimations predominated in Mattioni Maturana et al. (2), we contend that CP can both overestimate and underestimate the maximal metabolic steady-state depending on factors, such as the type of test, model, and fitting strategy used (3–5). In fact, an approximately 5% margin of error in CP estimation is acknowledged by others who support CP as the heavy–severe intensity boundary (6). While acknowledging that MLSS is itself an estimate of the maximal metabolic steady-state, in our view, MLSS is superior to CP testing because it simultaneously verifies whether the physiological responses conform to those expected at the critical intensity. Using MLSS “as the primary marker of the heavy–severe intensity boundary…is ironic given the arbitrary and highly liberal definition of MLSS.” There is no irony in using a delta change of 1 mmol·L−1 between 10 and 30 min as the criteria for a stable [La]. This is a well-established model. How liberal or conservative this measure needs to be can be debated, but normal measurement variability must be considered. Nevertheless, providing a physiological validation of the critical intensity of exercise is always more appropriate than accepting a model parameter estimate without any verification. “Based on available evidence…we contend that CP, when appropriately determined, is most representative of the upper limit of the metabolic steady state” This observation simply ignores several recent lines of evidence (2,3,7) and even common sense (i.e., how can metabolic steady-state be assumed without measuring metabolic responses to exercise?). We have discussed this topic in detail elsewhere (8). In short, Poole et al. (6) defined CP as “the highest intensity that can be sustained for a prolonged time solely by oxidative energy provision.” In this definition, exercise at CP does not draw upon anaerobic metabolism. Thus, progressive depletions in phosphocreatine and accumulations of [La] are not evident with time, which minimizes metabolic and acid–base disturbance and delays the initiation of fatigue. Therefore, the physiological responses expected at CP are those of MLSS. Any differences between these two indices simply relate to imprecisions inherent with the methods used for their determination. Although we believe that CP is a good approximation of the heavy–severe boundary, the clear limitations of this approach (2–4) make its use for research purposes inadequate, unless physiological validation is conducted to confirm metabolic stability at CP. To conclude, too often it is assumed that the model output estimate of CP reflects the true critical intensity of exercise (i.e., the heavy–severe boundary) despite compelling evidence that this is not always the case (2,3,7). Both MLSS and CP testing have inherent limitations, but in the absence of physiological verification, CP testing carries a greater degree of predictive uncertainty.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,017 | 0,018 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,091 | 0,066 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».