Critical prognostic factors for poststroke dysphagia: a meta-analysis.
Bibliographic record
Abstract
OBJECTIVE: Poststroke dysphagia (PSD) is one of the most significant problems after stroke. The prognosis of dysphagia is closely related to the outcomes of stroke. This meta-analysis aimed at identifying and evaluating critical predictors of prognosis for PSD. MATERIALS AND METHODS: Electronic databases were searched for relevant case-control and cohort studies in which the prognostic factors of PSD were reported. The methodological quality of the studies was assessed using the Newcastle-Ottawa Scale. Review Manager 5.3 was used to calculate odds ratios (OR) and their 95% confidence intervals (CI) of the included factors and to perform heterogeneity and sensitivity analyses. Stata 15.1 was used to evaluate publication bias. RESULTS: Eighteen of 3132 total studies were finally included in this meta-analysis. Ten predictors of PSD were identified, including 2 protective factors and 8 risk factors. Early intervention (OR=0.75, 95% CI=0.61-0.93) and an MRS (modified Rankin scale) score of 0 before onset (OR=0.58, 95% CI=0.47-0.71) were related to a better prognosis of PSD. The risk factors ranked by pooled OR values were aspiration (OR=7.64, 95% CI=5.94-9.82), brainstem injury (OR=4.82, 95% CI=3.01-7.72), severity of stroke (OR= 3.06, 95% CI=1.69-5.53), bihemispheric injury (OR=3.0, 95% CI=1.67-5.40), older age (OR=1.75, 95% CI=1.50-2.04), malnutrition (OR=1.36, 95% CI=1.22-1.53), severe dysphagia on admission (OR=1.16, 95% CI=1.03-1.29), and reduced level of consciousness (OR=1.03, 95% CI=1.00-1.07). CONCLUSIONS: Prognostic factors for a good outcome of PSD included early intervention and an MRS score of 0 before onset. Aspiration, brainstem injury, severe stroke and bihemispheric injury are the four most significant predictors of poor prognosis in PSD. Identifying these prognostic factors should help clinicians to better detect patients at risk and provide effective interventions for PSD.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.066 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".