The amount of visceral adipose tissue and the ratio of visceral to subcutaneous adipose tissue is greater in adults with vs. without spinal cord injury
Bibliographic record
Abstract
Abdominal fat increases cardiovascular disease (CVD) risk. Despite a higher prevalence of CVD in the chronic spinal cord injury (SCI) population, abdominal fat in those with vs. without SCI has not been determined. Our objectives were to (1) compare total (TAT), visceral (VAT), subcutaneous (SAT) and the ratio of visceral to subcutaneous (V/S) abdominal adipose tissue in adults with chronic SCI with that of age‐, sex‐ and waist circumference (WC)‐matched able‐bodied (AB) controls; and (2) determine the relationship between WC and VAT in both groups. 28 adults (14 SCI, 14 AB) participated in this cross‐sectional study. Abdominal fat was quantified by computed tomography. WC was measured at three sites (lowest rib, iliac crest, midpoint between the two). The V/S ratio was 45% greater in the SCI vs. AB group (0.48±0.23 vs. 0.33±0.14, p <0.05). After adjusting for differences in body weight, TAT and SAT were not different, however VAT was 64% greater in the SCI group ( p <0.01). WC at all sites was associated with VAT in both groups (SCI: 0.863 ≤ r ≤ 0.892, AB: 0.821 ≤ r ≤ 0.852, both p <0.001). Analysis of metabolic risk factors is in progress. The higher VAT and V/S ratio may explain, in part, the higher prevalence of CVD in those with SCI. WC appears to be a valid surrogate measure of VAT in this population, providing a tool for clinicians to identify those at risk for CVD. Research support: Canadian Foundation for Dietetic Research
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".