Changes in ventricular twist and untwisting with orthostatic stress: endurance athletes versus normally active individuals
Bibliographic record
Abstract
Endurance-trained individuals exhibit larger reductions in left ventricular (LV) end-diastolic volume in response to lower body negative pressure (LBNP) compared with normally active individuals. However, the relationship between LV torsion and untwisting and the LV volume response to LBNP in endurance athletes is unknown. Eight endurance-trained athletes [maximal oxygen consumption (VO2max): 66.4+/-7.2 ml.kg(-1).min(-1)] and eight normally active individuals (VO2max: 41.9+/-9.0 ml.kg(-1).min(-1)) (all men) underwent two cardiac magnetic resonance imaging (MRI) assessments, the first during supine rest and the second during -30 mmHg LBNP. Right ventricular (RV) and LV volumes were assessed, myocardial tagging was applied in order to quantify LV peak torsion and peak untwisting rate, and filling rates were measured with phase-contrast MRI. In response to LBNP, endurance-trained individuals had greater reductions in RV and LV end-diastolic volume and stroke volume (P<0.05). Endurance athletes had reduced untwisting rates (20.3+/-8.7 degrees/s), while normally active individuals had increased untwisting rates (-16.2+/-32.1 degrees/s) in response to LBNP (P<0.05). Changes in peak untwisting rate were significantly correlated with change in peak torsion (R=-0.87, P<0.05), with the change in early filling rate and VO2max, but not with changes in end-diastolic or end-systolic volume (P>0.05). We conclude that increased untwisting rates in normally active subjects may mitigate the drop in early filling rate with LBNP and thus may be a compensatory mechanism for the reduction in stroke volume with volume unloading. The opposite response in athletes, who showed a decreased untwisting rate, may contribute to their larger reductions in LV end-diastolic and stroke volumes with volume unloading and their orthostatic intolerance.
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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 | 0.001 |
| 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".