Obesity in acute ischaemic stroke patients treated with intravenous thrombolysis therapy
Bibliographic record
Abstract
OBJECTIVES: This article aimed to analyze the relationship between obesity and the efficacy of acute ischaemic stroke patients treated with IVT. BACKGROUND: Stroke causes morbidity and mortality in large numbers of individuals annually. Intravenous thrombolysis (IVT)with recombinant tissue plasminogen activator (r-tPA) is currently the only approved by the FDA for treatment of acute ischaemic stroke. Researchers have focused on studying the mechanisms associated with ischaemic stroke. Obesity is an established vascular risk factor with increasing prevalence and a huge impact on public health worldwide. It is an independent predictor for ischaemic stroke with a 4% risk increase for each unit augmentation in body mass index (BMI). Therefore, obese patients will constitute an increasing subgroup of candidates for IVT. However, its impact on prognosis in acute ischaemic stroke patients with intravenous thrombolysis did not reach a consensus conclusion. METHODS: Systematic literature search of PUBMED databases published before August 2020, was performed to identify studies addressing the role of obesity in acute ischaemic stroke patients treated with IVT. Studies included randomized clinical trials, observational studies, guideline statements, and review articles. CONCLUSIONS: Obesity may be related to long-term prognosis of large group of AIS patients treated with IVT. It depends on the scale of clinical study samples, follow-up time, and evaluation criteria.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".