Knee‐high compression socks minimize head‐up tilt‐induced cerebral and cardiovascular responses following dynamic exercise
Bibliographic record
Abstract
In healthy individuals during a non‐exercised state, knee‐high compression socks (CS) may reduce the magnitude of lower limb venous pooling during orthostasis but are not effective at minimizing the incidence of pre‐syncopal symptoms. However, exaggerated reductions in cerebral blood flow velocity (CBV) and cardiac stroke volume (SV) occur during passive head‐up tilt (HUT) testing following dynamic exercise. It is unknown ifCScan minimize post‐exerciseHUT‐induced decrements inCBVandSVin this population. To test the hypothesis thatCSwill attenuate the reductions inSVandCBVduring 60°HUTfollowing 60 minutes of moderate‐intensity (60%VO2peak) cycling exercise. Ten healthy volunteers (22.6 ± 2.1 years, 24.1 ± 2.5 kg/m2) completed pre‐ and post‐exercise 15‐minuteHUTtests during randomizedCSand Control (noCS) conditions. Changes in blood pressure (finger plethysmography),SV(Modelflow® method), andCBV(Transcranial Doppler) were measured duringHUTand preceding supine rest periods. Pre‐exerciseHUT‐induced similar (all,P > .47) reductions inSV(Control; −23.1 ± 11.5%,CS; −20.5 ± 10.9%) andCBV(Control; −18.1 ± 6.3%,CS; −15.3 ± 9.0%). However, larger post‐exercise decreases inSVandCBVduringHUTwere observed in the Control versusCScondition. Specifically,CSattenuated the drop inSV(Control: −32.9 ± 5.6%,CS: −24.3 ± 11.6%;P = .01) andCBV(Control: −25.1 ± 5.8%,CS: −17.6 ± 7.8%;P = .02) during the post‐exerciseHUTtest. These results indicate thatCSattenuatedHUT‐induced reductions inSVandCBVfollowing moderate‐intensity cycling exercise and suggest thatCSmay be an effective countermeasure to reduce the incidence of post‐exercise syncope in vulnerable populations.
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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) | 0.002 | 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".