Metabolic syndrome in people with a long-standing spinal cord injury: associations with physical activity and capacity
Bibliographic record
Abstract
This study investigated (i) the prevalence of the metabolic syndrome (MetS) in people with a long-standing spinal cord injury (SCI); (ii) whether personal or lesion characteristics are determinants of the MetS; and (iii) the association with physical activity or peak aerobic capacity on the MetS. In a cross-sectional study, persons with SCI (N = 223; time since injury of ≥10 years) were tested. The individual components of the MetS were assessed together with the physical activity measured by the Physical Activity Scale for Individuals with Physical Disabilities (PASIPD), while peak aerobic capacity was tested during a graded wheelchair exercise test on a treadmill. Thirty-nine percent of the participants had MetS. In a multivariate logistic regression analyses and after performing a backward regression analysis, only age and education were significant determinants of the MetS. A 10-year increase in age leads to a 1.5 times more chance to have the MetS. Furthermore, people with a low education will multiply the relative risk of MetS compared with people with high education by almost 2. With and without correcting for confounders, no significant relationship was found between PASIPD or peak aerobic capacity and the MetS. It can be concluded that the prevalence of the MetS is high (39%) in people with a long-standing SCI but is comparable to the general Dutch population. Older people and those with a lower education level are most at risk for the MetS. Physical activity and peak aerobic fitness were not related to the MetS in this group with a long-standing SCI.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".