Classification of obesity, cardiometabolic risk, and metabolic syndrome in adults with spinal cord injury
Bibliographic record
Abstract
Objective: To describe and compare (1) classification of obesity using clinical proxies of body composition that are easily accessible in the outpatient clinic setting, (2) cardiometabolic risk using existing screening tools and staging systems, and (3) the presence of metabolic syndrome (MetS) using four commonly-used definitions in adults with spinal cord injury (SCI). Design: Retrospective chart review Setting: Outpatient Veterans Affairs (VA) SCI Annual Evaluation Clinic Participants: Patients who attended an annual evaluation appointment with demographic, anthropometric, and biochemical data documented in their medical records as part of routine medical care. Outcome measures: Obesity classification (body mass index, waist circumference, ideal body weight percentage), cardiometabolic risk scores (Framingham Risk Score, Cardiometabolic Disease Staging System, Edmonton Obesity Staging System), and MetS classification (using four commonly-used definitions) were described and compared. Results: Of the 155 veterans included in this analysis, 93% were considered “at risk” by at least one of the measurements studied. However, there was considerable variation between the different screening tools. The κ-agreement between various definitions of MetS ranged from fair to moderate. Conclusion: Screening tools that were developed for the non-SCI population produced variable assessments of risk when applied to veterans with SCI. Due to the fair to moderate inter-rater agreement between MetS definitions, it is unknown which definition is superior to identify MetS in the SCI population. An SCI-specific screening tool is needed to accurately classify obesity, cardiometabolic risk, and MetS in order to provide timely education and intervention.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".