Arterial Stiffness in Persons with SCI: A Pilot Study
Bibliographic record
Abstract
Low cardiovascular fitness and abdominal obesity are risk factors for increasing arterial stiffness, and cardiovascular disease (CVD) in the able-body population. These risk factors are also common among people with spinal cord injury (SCI) whom also have an elevated risk of CVD. PURPOSE: To test the hypothesis that SCI subjects have higher arterial stiffness and CVD risk than age-gender matched non-SCI subjects, assessed by pulse wave velocity (PWV) in cm/sec. METHODS: Subjects included 12 men with motor complete SCI (Age: 51+−11 yrs, Height: 179+−6.6cm; Body weight: 84.0+−20.2 kg, Injury Level: C3-L3, Time post injury: 25+−10.7 yrs) and eight Non-SCI controls (Age: 45+−10 yrs; Height: 175.1+− cm; Body weight: 75.6+−10.9 kg). PWV of the aorta was measured between the carotid and femoral artery as an index of arterial stiffness using the echo doppler method. Trunk adiposity was assessed using Dual-energy X-ray Absorptiometry. Peak oxygen uptake (Vo2peak: mL/kg/min) was assessed via arm ergometry. RESULTS: There were no statistically significant differences between SCI subjects and Non-SCI peers in age, height and body weight, despite what appears to be important baseline clinical differences between the groups. PWV in SCI subjects (1331+−327 cm/sec) were significantly higher (P<0.05) than that of Non-SCI peers (951+−335 cm/sec). Trunk fat mass in SCI subjects (14.7+−7.0 kg) was larger (P<0.05) than that of Non-SCI peers (7.0+−3.4 kg). Vo2peak in SCI subjects (15.9+−4.7 mL/kg/ min) was lower than that of Non-SCI peers (18.3+−8.4 mL/kg/min). CONCLUSIONS: The higher PWV in SCI subjects compared to Non-SCI subjects may suggest a higher risk of CVD among SCI subjects. Although further data collection is required, PWV has the potential to become a good diagnostic test for CVD among people with SCI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".