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Enregistrement W4256719001 · doi:10.1249/mss.0000000000000677

Response

2015· letter· en· W4256719001 sur OpenAlexaffabout
Daniel A. Keir, Federico Y. Fontana, Taylor C. Robertson, Juan M. Murias, Donald H. Paterson, John M. Kowalchuk, Silvia Pogliaghi

Notice bibliographique

RevueMedicine & Science in Sports & Exercise · 2015
Typeletter
Langueen
DomaineMedicine
ThématiqueCardiovascular and exercise physiology
Établissements canadiensUniversity of CalgaryWestern University
Organismes subventionnairesnon disponible
Mots-clésRespiratory compensationCorrelationMedicineInternal medicineStatisticsMathematicsPsychologyPhysical therapy

Résumé

récupéré en direct d'OpenAlex

Dear Editor-in-Chief: The purpose of our recent study (6) was to establish, in a single group of subjects, whether various indices identifying boundaries of sustainable performance shared common characteristics (e.g., pulmonary O2 uptake (V˙O2p)), this being done before attempting to address a mechanistic basis for these indices. Indeed, we showed with a high level of accuracy and precision that the V˙O2p associated with each of critical power (CP), maximal lactate steady state, respiratory compensation point (RCP), and the near-infrared spectroscopy-derived muscle deoxygenation breakpoint were not different (6). Given that in our carefully controlled study, each of the examined “thresholds” occurred at similar V˙O2p values, we believed that this observation could not be ignored and that speculation regarding a common underlying physiological mechanism among indices was warranted. However, Craig et al. (5) strongly oppose this possibility on the basis of their findings, reporting i) an absence of correlation between parameters (specifically RCP and CP) (4) and ii) the existence of a high degree of intrasubject variability among selected parameters (3). First, we question the use of correlational analyses to test correspondence between parameters, where Bland–Altman analysis is considered more appropriate (1). Second, the lack of correlation between V˙O2p associated with CP and RCP in the study of Broxterman et al. (4) may be partly attributable to measurement variability associated with CP, as the variability of V˙O2p at CP seems to be twice that of RCP. In our study, variability was narrower and consistent among all parameters and, as a consequence, minimized the likelihood of a type II error. Therefore, contrary to Craig et al. (5), “absence of evidence” can be considered “evidence of absence,” and thus, the lack of difference among parameters in our study may be appropriately interpreted as equivalence. Regarding the “disservice to the readership by ignoring recent work,” we were unaware of the article by Boone et al. (2) that appeared online a few days before our original submission and was regretfully missed during revision. However, we cannot apologize for lacking the clairvoyance to anticipate the publication of the study of Broxterman et al. (4), which became available online only 4 d before final acceptance of our article. Finally, by no means do we proclaim that our article is the “final word” on the association among these indices, nor do we believe that the issue related to the equivalence between these paradigms is “settled.” That an abundance of methods and indices (each with their own nomenclature) exist to define the intensity beyond which physiological homeostasis can no longer be maintained limits the comparability of data and the availability of a common reference point for subject evaluation, training design, and exercise prescription. In contrast to the opinion of Craig et al. (5), we believe that our study does provide meaningful contribution to this body of literature and does so in a nondictatorial manner. Future work should strive to examine and include the observable “threshold-like” physiological phenomena associated with high intensities of exercise to uncover their mechanistic bases—only then can equivalence or coincidence of their manifestation be concluded. Daniel A. Keir Canadian Centre for Activity and Aging School of Kinesiology University of Western Ontario London, Ontario, CANADA Department of Neurological and Movement Sciences University of Verona Verona, ITALY Federico Y. Fontana Department of Neurological and Movement Sciences University of Verona Verona, ITALY Taylor C. Robertson Canadian Centre for Activity and Aging School of Kinesiology University of Western Ontario London, Ontario, CANADA Juan M. Murias Faculty of Kinesiology University of Calgary Calgary, Alberta, CANADA Donald H. Paterson John M. Kowalchuk Canadian Centre for Activity and Aging School of Kinesiology Department of Physiology and Pharmacology University of Western Ontario London, Ontario, CANADA Silvia Pogliaghi Department of Neurological and Movement Sciences University of Verona Verona, ITALY

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,024
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,307
Score d'incertitude au seuil0,000

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,024
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,001
Communication savante0,0020,003
Science ouverte0,0020,002
Intégrité de la recherche0,0070,007
Charge utile insuffisante (le modèle a refusé de juger)0,3070,178

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.

Tête enseignante Opus0,023
Tête enseignante GPT0,294
Écart entre enseignants0,271 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2015
Routes d'admission2
Résumé présentoui

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