Influence of dynamic cerebral autoregulation on presyncope in endurance athletes (1184.5)
Bibliographic record
Abstract
The impact of dynamic cerebral autoregulation (dCA) on presyncope symptoms (PS) is controversial in endurance athletes. We examined the influence of dCA during transient hypotension on PS in 11 male athletes using 1) the thigh‐cuff method (rate of regulation [RoR]), and 2) the relative reduction in cerebrovascular conductance index (CVCi) during transient hypotension induced by squat‐stand maneuvers at different frequencies (0.025, 0.05 and 0.1 Hz). Prevalence of PS was assessed using a 20‐min glyceryl trinitrate‐provoked head‐up tilt. Correlations were assessed between dCA metrics. Subjects were then divided into two groups according to presence (n=6; PS group) or absence (n=5; non‐PS group) of PS. There were no correlations between dCA metrics and time to PS. dCA metrics were unrelated, except CVCi response to hypotension at 0.05 Hz and 0.1 Hz (B=0.846, p=0.008). ROR was comparable between PS and non‐PS groups. The CVCi response to hypotension was attenuated in the PS group vs. non‐PS group (0.05 Hz: 1.51 ± 0.31 vs. 2.47 ± 0.27 %/%; 0.1 Hz: 1.35 ± 0.36 vs. 2.32 ± 0.41 %/%, all p<0.05). These results suggest that although CVCi was better adapted to coping with transient hypotension in athletes with PS vs. those without PS, this was unrelated to PS. Thus, metrics of dCA assessing the cerebral blood flow response to hypotension cannot be used to predict PS risk in endurance athletes. Grant Funding Source : Supported by a research contract from the Ministère de l'Éductation, du Loisir et du Sport (Québec)
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".