Comparison of Cognitive Functions between Paroxysmal and Persistent Atrial Fibrillation Patients without Clinical Stroke
Bibliographic record
Abstract
Objective: To compare cognitive functions between paroxysmal and persistent atrial fibrillation patients without clinical stroke, in terms of MoCA-Ina total score and MoCA-Ina cognitive subdomains scores. Methods: A comparative study that compared MoCA-Ina scores between paroxysmal and persistent AF patients without clinical stroke, who came for treatment at the Cardiology Clinic Dr. Hasan Sadikin General Hospital, Bandung and Dustira Hospital, Cimahi from September 2018–January 2019. Results: Sixty four subjects were recruited, consisted of 24 paroxysmal and 40 persistent AF patients. There were no difference in clinical characteristics between two groups, except that there were more subjects in the persistent AF group using anticoagulants therapy: 97.5% vs 62.5% (p=0.005) and more prevalence of type 2 Diabetes Mellitus in the paroxysmal AF group: 25% vs 2.5% (p=0.009). Cognitive impairment (MoCA-Ina score <25) were seen in 70.8% of paroxysmal AF group and 82.5% of persistent AF group (p=0.274). The mean MoCA-Ina total score in the paroxysmal and persistent AF groups were 21.04±4.75 vs 20.70±4.21 (p=0.989), respectively. The median MoCA-Ina cognitive subdomains scores were similar for the two groups (p>0.05).Conclusion: There were no differences in cognitive functions between paroxysmal and persistent AF patients, both in terms of MoCA-Ina total score and MoCA-Ina cognitive subdomains scores, although in both groups had cognitive decline.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".