Post-Doc Competition (Knowledge Generation) ID 1985177
Bibliographic record
Abstract
Background Heart Disease is the leading cause of death after spinal cord injury (SCI). Individuals with paraplegia develop hypertension and elevated arterial stiffness prior to their age matched peers. Overground exoskeleton training is becoming an increasingly prevalent form of exercise. In the general population, exercise training has been shown to reduce arterial stiffness. Recent RCT failed to show changes in arterial stiffness with arm ergometry or body weight supported treadmill training. Objectives The aim of this project is to determine the effect of Exoskeleton Exercise on changing arterial stiffness. Study Design The study setting will take place at a rehabilitation facility. The study will involve 34 sessions over the span of approximately 18-20 weeks. The key intervention utilized in the investigation will be an Overground EksoNR gait and balance training program. The primary outcome measure will be carotid-femoral pulse wave velocity (cfPWV) and secondary measures will include heart rate (HR), blood pressure (BP), waist circumference (WC), and oxygen saturation (SpO2). Statistical Analysis will involve mean changes at baseline, midway, and end of study. Methods The 34-session (2x/week for ∼18 weeks) rehabilitation protocol will focus on using the EksoNR for overground gait and balance activities. For measurement of cfPWV, two transcutaneous Doppler flowmeters will be used at the common carotid and femoral artery. To calculate cfPWV, the distance travelled by the pulse is divided by the average pulse transit time (PTT). Hypothesis The 18-week Exoskeleton Program would improve arterial stiffness (cfPWV) in individuals with chronic incomplete SCI/D.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.948 | 0.877 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".