The Impact of Body Mass Index Classification on Outcomes After Stroke Rehabilitation
Bibliographic record
Abstract
ABSTRACT: With improving stroke mortality rates, more individuals are living with the consequences of stroke. Obesity is a known risk factor for stroke, but its effect on functional outcomes poststroke is less clear. The aim of this study was to determine the association between body mass index classification (underweight, normal weight, overweight, and obese) and functional outcomes, as measured by Functional Independence Measure change, Functional Independence Measure efficiency, and rehabilitation length of stay after inpatient stroke rehabilitation. A retrospective cohort study of individuals with a diagnosis of stroke admitted to a high-intensity inpatient rehabilitation program was performed. Patients were divided into 4 groups based on body mass index category using normal body mass index as the reference. Overall, 34 individuals (4.5%) were classified as underweight, 303 (40.1%) had body mass indices within the normal range, 269 (35.6%) were overweight, and 149 (19.7%) were obese. Ischemic stroke was the most common stroke type across all body mass index categories. Patients in the overweight and obese groups tended to be younger. There were no statistically significant differences in rehabilitation length of stay, Functional Independence Measure change, or Functional Independence Measure efficiency with all groups demonstrating significant functional improvements. Based on these findings, patients admitted for inpatient rehabilitation after stroke can be expected to make similar functional improvements regardless of BMI class.
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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 | 0.001 | 0.005 |
| 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 | 0.000 | 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".