Response
Notice bibliographique
Résumé
Dear Editor-in-Chief: We appreciate the interest of Bain et al. (2) in our work (6). Recently, Bain et al. (1) argued that the reported reductions in body heat storage with cold water ingestion (7,8) were likely due to the inherent underestimation of body heat storage associated with thermometry (5). To circumvent this problem, they used partitional calorimetry to assess changes in body heat storage with intermittent ingestion of water of various temperatures during a 75-min exercise bout. In contrast to previous reports, they showed a disproportionate increase in evaporative heat loss (as estimated by changes in body weight) with ingestion of hot (50°C) relative to cold (1.5°C) water, which amounted to reductions in body heat storage. They repeated a similar protocol (9), which also measured local sweat rate (LSR) at multiple sites. In addition to whole-body sweat losses that paralleled their previous work (1), they observed transient increases and decreases in LSR after each bolus of hot and cold water, respectively, compared with water ingested at 37°C. Importantly, reflex changes occurred in both cases despite similar core and skin temperatures between conditions, ultimately failing to resolve the debate on the ingestion of hot versus cold water in the context of body heat storage. Noteworthy, they reported a similar pattern of response for LSR when water was delivered directly to the stomach via a nasogastric tube, but not when water was swilled within the mouth only, highlighting the importance of gastrointestinal thermoreceptors in modulating sweating. As recently discussed by Kenny and Jay (5), direct calorimetry is a precise way of measuring real-time changes in whole-body sweat rate (WBSR) under conditions permitting full evaporation. Using this technique, we observed an elevated WBSR with hot, relative to cold, water ingestion (6), albeit our measurements of LSR at sites similar to those of Morris et al. (9) yielded inconsistent results. However, our findings support those of Morris et al. (9) in showing that reflex changes in LSR are also observed at the whole-body level with hot and cold water ingestion in the absence of differences in core and skin temperatures. In addition, although our observations for WBSR are consistent with the findings of Bain et al. (1), we reported a proportionate adjustment in heat exchange that led to no differences in body heat storage following exercise, whereas Bain et al. (1) observed a reduction in body heat storage with hot, relative to cold, water ingestion. Presumably, our studies should have arrived at similar conclusions if WBSR were accurately assessed, especially given that both partitional and direct calorimetry rely on environments that permit complete sweat evaporation. In their letter, Bain et al. (2) indicated that their conditions were likely not suitable for ensuring 100% sweat evaporation, despite choosing experimental conditions to accommodate this limitation. It is unclear whether the addition of strategically placed fans would eliminate these conflicting findings. Regardless, we concur that cold water ingestion is the obvious choice under conditions that restrict heat loss, and we strongly recommend cold water under any condition given the many benefits that may impact exercise performance (3,4). Dallon T. Lamarche Robert D. Meade Ryan McGinn Martin P. Poirier Brian J. Friesen Glen P. Kenny Human and Environmental Physiology Research Unit School of Human Kinetics, University of Ottawa Ottawa, ON, CANADA [email protected]
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 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,007 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,352 | 0,204 |
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 ».