The Impact of Modified Body Mass Index on Clinical Prognosis in the Elderly With Acute Ischemic Stroke
Bibliographic record
Abstract
OBJECTIVES: The modified body mass index (mBMI) combines the body mass index and serum albumin, accurately reflecting the nutritional status. It remains uncertain whether modified body mass index influences neurological function and clinical prognosis in elderly patients with acute ischemic stroke. METHODS: We divided the cohort into quartiles of mBMI (1 to 4). The primary outcome was analyzed using the percentage of patients with a 90-day modified Rankin scale (mRS) score of 0 to 1. There were 7 secondary outcomes, including the disability level at 90 days and the National Institute of Health Stroke Scale (NIHSS) score at 14 and 90 days. RESULTS: mBMI was negatively associated with clinical prognosis at 90-day mRS score in the primary outcome (β=-0.167; 95% CI -0.311 to 0.023, P =0.023). Moreover, mBMI1 (<896.72) and primary outcomes (β=0.438; 95% CI: -0.018 to 0.894) were positively correlated with higher mBMI. Moreover, the number and percentage of patients completing all the duties and activities are also higher. Age-adjusted Charlson comorbidity index (aCCI) and posterior circulation lesion were positively associated with the clinical prognosis 90-day mRS score in the primary outcome (β=2.218; 95% CI: 1.144-4.300, β=2.771; 95% CI: 1.700-4.516). However, BMI and serum albumin were not associated the with clinical prognosis primary outcome. BMI negatively correlates with secondary outcomes (NIHSS at discharge, β=-0.023; 95% CI: -0.102 to 0.057). CONCLUSIONS: Our study revealed that mBMI and not BMI could be a better primary outcome predictor in the elderly with acute ischemic stroke, and lower mBMI showed a worse prognosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".