Risk factors of dysphagia in patients with ischemic stroke: A meta-analysis and systematic review
Bibliographic record
Abstract
BACKGROUND: Dysphagia is a common yet serious complication in stroke patients. We aimed to conduct a meta-analysis and systematic review to evaluate the risk factors of dysphagia in patients with ischemic stroke, to provide insights to the clinical treatment and nursing care of dysphagia. METHODS: We searched PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI) and Wanfang Database, China Biomedical Literature Database (CBM) for studies on dysphagia in patients with ischemic stroke up to January 31, 2022. The quality of the literature was evaluated using the Newcastle-Ottawa scale. Meta-analysis was performed using RevMan 5.3 software. RESULTS: A total of 10 studies involving 4637 ischemic stroke patients were included, 1183(25.51%) patients had dysphagia after stroke. The synthesized outcomes showed that elder age (SMD = 0.42, 95%CI:0.34-0.50), hypertension (OR = 1.96, 95%CI:1.48-2.61), diabetes (OR = 1.83, 95%CI:1.47-2.28), brainstem stroke (OR = 2.12, 95%CI:1.45-3.09) were associated with dysphagia in patients with ischemic stroke (all P<0.05). There was no significant difference in the gender between dysphagia and no dysphagia patients (OR = 1.07, 95%CI:0.91-1.27, P = 0.40). Egger regression tests indicated there were no significant publication biases in the synthesized outcomes (all P>0.05). CONCLUSIONS: Elder age, hypertension, diabetes and brainstem stroke are associated with the development of dysphagia in patients with ischemic stroke. Attention should be paid to the assessment and early intervention of those risk factors for dysphagia to improve the prognosis of stroke patients.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.041 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".