<p>Risk of Atrial Fibrillation, Ischemic Stroke and Cognitive Impairment: Study of a Population Cohort ≥65 Years of Age</p>
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: To evaluate a model for calculating the risk of AF and its relationship with the incidence of ischemic stroke and prevalence of cognitive decline. MATERIALS AND METHODS: It was a multicenter, observational, retrospective, community-based study of a cohort of general population ≥6ct 35 years, between 01/01/2016 and 31/12/2018. Setting: Primary Care. Participants: 46,706 people ≥65 years with an active medical history in any of the primary care teams of the territory, information accessible through shared history and without previous known AF. Interventions: The model to stratify the risk of AF (PI) has been previously published and included the variables sex, age, mean heart rate, mean weight and CHA2DS2VASc score. Main measurements: For each risk group, the incidence density/1000 person/years of AF and stroke, number of cases required to detect a new AF, the prevalence of cognitive decline, Kendall correlation, and ROC curve were calculated. RESULTS: The prognostic index was obtained in 37,731 cases (80.8%) from lowest (Q1) to highest risk (Q4). A total of 1244 new AFs and 234 stroke episodes were diagnosed. Q3-4 included 53.8% of all AF and 69.5% of strokes in men; 84.2% of all AF and 85.4% of strokes in women; and 77.4% of cases of cognitive impairment. There was a significant linear correlation between the risk-AF score and the Rankin score (p < 0.001), the Pfeiffer score (p < 0.001), but not NIHSS score (p 0.150). The overall NNS was 1/19. CONCLUSION: Risk stratification allows identifying high-risk individuals in whom to intervene on modifiable risk factors, prioritizing the diagnosis of AF and investigating cognitive status.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it