Demographic and clinical factors associated with recovery of poststroke dysphagia: A meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Poststroke dysphagia (PSD) recovery depends on various factors. We aimed to provide evidence concerning predictive variables for the recovery of PSD. METHODS: PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), Wanfang Database, VIP database of Chinese periodicals, Chinese biomedical literature service system (SinoMed), and Cochrane Library databases were systematically searched up to September 21, 2022. According to the inclusion criteria, the literature searched in the database was screened. The methodological quality of included studies was assessed using the Newcastle-Ottawa Scale (NOS). Meta-analysis was performed to identify the factors prognostic for PSD. RESULTS: Twenty-eight studies were eligible, and pooled analyses were allowed for 12 potential prognostic factors. We identified older age, higher National Institutes of Health Stroke Scale (NIHSS) score, lower activities of daily living (ADL) score, lower body mass index (BMI), severe dysphagia on admission, aspiration, brainstem stroke, severe cognitive impairment, and bilateral hemispheric stroke were negative factors for the recovery of PSD, while early intervention and Modified Rankin Scale (mRS) = 0 before onset were protective factors for the recovery of PSD. There was no significant association between stroke type and prognosis of PSD. CONCLUSION: Prognostic factors of PSD summarized in this meta-analysis could be useful for developing reasonable treatment plan to better promote recovery of swallowing function after stroke.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.035 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".