A Systematic Review and Meta-analysis to Determine the Effect of Oral Anticoagulants on Incidence of Dementia in Patients with Atrial Fibrillation
Bibliographic record
Abstract
Aims: To assess the effect of oral anticoagulant (OAC) administration on incidence of dementia in patients with atrial fibrillation (AF) with Systematic review and meta-analysis in according with the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols. Methods: We systematically searched the electronic databases including Pubmed, Embase, Cochrane library, and ClinicalTrails.gov for relevant articles. The primary outcome was the incidence of dementia. The adjusted risk ratio (RR), odds ratio, or hazard ratio were extracted and pooled by the random-effects models. Subgroup analysis was performed according to the setting observational window. Risk of bias was assessed using the Newcastle-Ottawa Scale, while publication bias was assessed by the Begg’s and Egger’s tests. Results: Nine studies included in this review (2 prospective and 7 retrospective observational studies, including 613,920 patients). The results presented the significant association between OAC therapy and the reduced risk of dementia compared with no treatment (RR [95%CI] =0.72 [0.60, 0.86], I2=97.2%; P =0.000). In the subgroup analysis, the pooled RR became statistically non-significant (including four studies, RR [95%CI] =0.75 [0.51, 1.10], I2=98.8%; P =0.000). There is no significant risk of bias and publication bias. Conclusions: This study indicated the protective effect of OAC therapy for dementia in patients with AF. However, the results are limited because of high heterogeneity, inconsistent direction of effect in subgroup analysis. Further prospective well-designed study is needed with longer follow-up duration in younger patients.
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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 | 0.022 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.029 | 0.047 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| 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".