Measures of general and abdominal obesity and disability severity in a large population of people with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Metabolic comorbidity is overrepresented in people with multiple sclerosis (MS) and is associated with adverse MS outcomes. Excess visceral adiposity, approximated using waist circumference (WC), is a risk factor for metabolic comorbidity and predicts poorer outcomes in other neurologic diseases. OBJECTIVE: To evaluate the association between WC and clinical and disease characteristics in people with MS. METHODS: North American Research Committee on MS (NARCOMS) registry participants reported height and weight (used to calculate body mass index (BMI)) and were mailed a tape measure with instructions to measure WC. We considered WC continuously and used cut-points derived from the abdominal obesity criteria for the metabolic syndrome (men: WC ⩾ 40 in; women: WC ⩾ 35 in). We assessed the association between WC and disability (Patient-Determined Disease Steps) and symptom severity (validated scales) using multivariable-adjusted multinomial models. RESULTS: Of 6367 responders with MS, we included 5832 (92%). Of these, 3181 (55%) reported WC meeting criteria for the abdominal obesity component of metabolic syndrome. In multivariable models adjusting for overall obesity status, WC was associated with 47% increased odds of severe versus mild disability (odds ratio (OR): 1.47; 95% confidence interval (CI): 1.22-1.78). CONCLUSIONS: Increased WC is associated with more severe disability, even after adjusting for overall obesity in this large cross-sectional survey.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".