Body Mass Index and Acute Ischemic Stroke Outcomes
Bibliographic record
Abstract
Background The impact of body mass index on acute ischemic stroke outcomes is unclear. Aims and/or hypothesis We sought to determine the effect of body mass index on short-term (90 days) acute ischemic stroke outcomes. Methods Data were extracted for patients with acute ischemic stroke and records of body mass index at baseline from the Virtual International Stroke Trials Archive database. Multivariate logistic regression and Cox proportional hazard analysis were used to analyze effect of body mass index on poor functional outcome (modified Rankin Scale >2) and mortality, respectively, within 90 days of stroke's onset. Results Of the 4811 patients (mean age 68·8 ± 12·2 years) included in the study, 2002 (41·6%) were overweight, and 1095 (22·8%) were obese. Overweight (body mass index 25-29·9 kg/m2) was associated with decreased mortality (hazard ratios 0·59; 95% confidence interval 0·51-0·68; P < 0·01) and decrease in poor functional outcome (odds ratio 0·74; 95% confidence interval 0·64-0·85; P < 0·01) following acute ischemic stroke. The association of body mass index with stroke outcomes was dependent on age, gender, and use of thrombolytic therapy. Conclusions Being overweight or obese is associated with a better functional outcome and reduced mortality in patients of acute ischemic stroke. However, the definition of an 'optimal' body mass index, in relation to stroke outcomes, may be affected by age, gender, and use of thrombolytic therapy.
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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 | 0.006 |
| 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 | 0.001 | 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".