53 (10A) Effects of lower body negative pressure and head-up tilt cycling on exercise tolerance; an assessment of post-concussion symptoms
Bibliographic record
Abstract
Purpose This study investigated the effects of lower body negative pressure (LBNP) during supine cycling as a novel, non-pharmacological approach aimed at improving recovery from concussion. While exercise is well-documented as beneficial for brain health and concussion recovery, it may exacerbate symptoms due to increased cerebral blood flow inside the enclosed cranium (Monro-Kellie Doctrine), specifically ‘ headaches’ and ‘pressure in the head’.Methods 23 individuals (10M/13F, ages 17–45) with persisting post-concussion symptoms (PPCS: 0.5–12 years post-injury) participated in two randomized visits, separated by a week. A submaximal ramp-incremental exertion test (Calgary Concussion Cycle Test) was performed during both visits and included no pressure (sham) or LBNP (experimental) with head-up tilt. Middle cerebral artery velocity (MCAv) was measured using Transcranial Doppler ultrasound, heart rate (HR) via 3-lead ECG, symptoms were assessed using the SCAT6 before, and at 0-, 10-, and 60- minutes post-exertion. Total exposure was indexed as area-under-the-curve (AUC) from baseline across exercise stages.Results MCAv AUC revealed the LBNP intervention substantially blunted MCAv relative to baseline (8.82% MCAv stages) compared to sham (134.98% MCAv stages) while HR AUC revealed a greater heart rate occurred during the LBNP condition (153.76% HR stages). Exploratory analysis on individual symptoms revealed ‘pressure in the head’ was reduced at the 60 min post-exertion time for LBNP condition (p= 0.032).Conclusion LBNP reduced MCAv while augmenting HR during exercise, resulting in fewer symptoms one-hour afterward. This approach shows promise for a novel recovery method for improving outcomes from concussion, especially for PPCS.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.001 |
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".