Relationship between Post-Stroke Cognitive Impairment and Severe Dysphagia: A Retrospective Cohort Study
Bibliographic record
Abstract
Objective: To investigate the relationship between post-stroke cognitive impairment (PSCI) and severe post-stroke dysphagia (PSD) and explore the risk factors related to PSCI combined with severe PSD. Methods: Data from patients were collated from the rehabilitation-specific disease database. The Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Videofluoroscopy Swallowing Study (VFSS), Penetration-aspiration Scale (PAS), and Functional Oral Intake Scale (FOIS) were used to evaluate cognitive and swallowing functions. Differences between groups were determined by the Pearson chi-square test (χ2) or Fisher exact test. PAS and FOIS data were analyzed with the use of the Wilcoxon rank-sum or Kruskal−Wallis test in the prespecified subgroup analysis. Risk factors were investigated by multivariate logistic regression. Results: A total of 1555 patients were identified with PSCI. The results indicated that patients with PSCI had a higher incidence rate of severe PSD as compared to patients without PSCI (p < 0.001). Patients with severe PSCI were more likely to clinically manifest oral phase dysfunction (p = 0.024), while mild PSCI patients mainly manifested pharyngeal phase dysfunction (p < 0.001). There was a significant difference in FOIS score changes between subgroups during the hospitalization period (severe PSCI vs. moderate PSCI and severe PSCI vs. mild PSCI) (all p < 0.001). In addition, multivariate logistic regression revealed pneumonia (p < 0.001), tracheotomy (p < 0.001), and dysarthria (p = 0.006) were related to PSCI, combined with severe PSD. Conclusion: PSCI may be related to severe PSD. Patients with severe PSCI were more likely to manifest oral phase dysfunction, while mild PSCI manifested pharyngeal phase dysfunction. Pneumonia, tracheotomy, and dysarthria were risk factors related to PSCI combined with severe 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".