Impact of Pre-Stroke Dementia or Mild Cognitive Impairment on Stroke Outcome: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Pre-stroke dementia (PSD) and pre-stroke mild cognitive impairment (PSMCI) are important risk factors for stroke. The present meta-analysis aimed to investigate the impact of PSD or PSMCI on stroke outcomes. METHODS: Electronic databases (PubMed, EMbase, Google Scholar, Cochrane Library, and TRIP) were screened for eligible studies published prior to March 31, 2021. Risk ratios (RR) and mean differences with 95% confidence intervals (CIs) using random or fixed effect models were used to calculate pooled estimates. Study quality was assessed using the Newcastle Ottawa Scale. RESULTS: Fifteen studies were included in our meta-analysis. Pooled data from ten studies involving 3,107 PSD and 20,645 non-PSD subjects showed a higher risk of mortality in PSD patients (RR = 2.03; 95% CI: 1.40-2.91; I2 = 89%). Risk of recurrent stroke risk was observed more in patients with PSD compared to non-PSD patients (RR = 2.02; 95% CI: 1.40-2.91; I2 = 0%). Three studies involving 300 mild cognitive impairment (MCI) and 1,025 normal cognition subjects showed a significant increased risk of mortality in stroke patients with MCI (RR = 2.43; 95% CI: 1.81-3.27; I2 = 20%). However, elevated stroke severity was not observed in PSMCI patients. CONCLUSIONS: Our meta-analysis shows an increased risk of mortality in stroke patients with a history of PSD and PSMCI. Proper clinical management and increased attention are therefore required for the prevention and management of stroke in patients with cognitive deficits.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.044 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".