Abstract TMP101: Post-stroke Cognitive Impairment And The Risk Of Recurrent Stroke And Death: Systematic Review And Meta-analysis
Bibliographic record
Abstract
Post-stroke cognitive impairment (PSCI) occurs in 20-40% of patients 3-6 months after stroke. However, the question regarding risks of recurrent stroke and death in patients with PSCI remains controversial. The goal of this study was to conduct meta-analysis of published literature to estimate the risks of stroke recurrence and death associated with PSCI, as well as to assess the quality of these studies, and to identify sources of variation. Electronic databases (PubMed, EMbase, Google Scholar, Cochrane Library, and Scopus) were screened for eligible studies published from 1992 to 2019. Study quality was assessed using the Newcastle Ottawa Scale. Risk of bias in non-randomized and randomized studies was assessed using ROBINS-E and RoB 2 tools, respectively. Funnel plots and selection models were used for evaluating publication bias. Heterogeneity was assessed using a combination of statistics: I 2 , Q -statistic, and Kendall τ 2 . Estimates were obtained using a random effects model and restricted maximum likelihood estimation. Pooled estimates for the two outcomes of interest were calculated as hazard ratios (HR) with 95% confidence intervals (CIs). We included eight studies examining the effect of PSCI on risk of stroke recurrence. We found no evidence of potential publication bias among the included studies in stroke recurrence (χ 2 =3.440, p=0.487) and mortality (χ 2 =3.797, p=0.150). Pooled data from the eight studies involving n=1,840 PSCI and n=5,824 non-PSCI showed that the hazard of recurrent stroke risk was significantly higher in individuals with PSCI compared to non-PSCI participants (HR = 1.71; 95% CI: 1.45 - 2.01; I 2 = 0%). Seventeen studies were included examining the impact of PSCI on mortality risk. The pooled data from these studies comprised n=7,591 PSCI and n=22,328 non-PSCI study participants. The pooled hazard of mortality was significantly higher in the PSCI group relative to the non-PSCI group (HR = 1.98; 95% CI: 1.64 - 2.40; I 2 = 82.95%). This meta-analysis shows an increased risk of mortality and recurrent stroke in patients with PSCI. Post-stroke cognitive testing may identify patients at a higher risk of stroke recurrence and death who may require more aggressive interventions for secondary prevention.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.008 | 0.009 |
| 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) | 0.005 | 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".