Habitual exercise in youth: A ʻbrainyʼ idea
Bibliographic record
Abstract
cerebral blood flow, cerebrovascular reactivity, exercise, hypercapnia, maturation, paediatricThe impact of exercise training on cerebrovascular health is a hot topic, with an increasing focus on preventing and delaying neurodegenerative diseases.Yet, there is a paucity of information regarding the effect of habitual exercise on cerebrovascular function, particularly in children and adolescents.This age range is often under-represented in research but can offer important insight concerning the trajectory of health.It is also unclear what the consequences of a lack of habitual exercise are for cerebrovascular health in youth.To address this gap in the literature, in an article in this issue of Experimental Physiology, Talbot et al. investigated both cerebral blood flow (CBF) and cerebrovascular reactivity to carbon dioxide in youth, while accounting for different stages of maturation (using a somatic measure of maturity -predicted age at peak height velocity or PHV) (Talbot, Perkins, Tallon et al., 2023).This study required a considerable number of participants to account for sex, maturation and training status.The authors reported novel findings that endurance-trained youth had higher global CBF at rest (using internal carotid artery (ICA) + vertebral artery blood flow assessments) in comparison to untrained counterparts.In the ʻmatureʼ adolescents, cardiorespiratory fitness was associated with global CBF.Post hoc analysis revealed that untrained post-PHV (or ʻmatureʼ) males demonstrated lower global CBF compared with trained post-PHV males, with no effect of training found in post-PHV females or younger (pre-PHV) groups.Untrained youth were defined as ʻnot taking part in regular exerciseʼ or not meeting established guidelines for physical activity.Although
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".