Predictors of Hospital Mortality in Patients with Atrial Fibrillation and Stroke
Bibliographic record
Abstract
Atrial fibrillation is one of the most common cardiac arrhythmias; it accounts for about a third of all hospital admissions for cardiac arrhythmias. Currently, there is a tendency of aging of the population and an increase in overall life expectancy, which will further lead to an increase in the number of patients with atrial fibrillation.Usually atrial fibrillation is associated with a number of symptoms such as palpitations, interruptions, shortness of breath, pain in the heart area, fatigue, dizziness and syncopal states, but at the same time the course of both paroxysmal and permanent atrial fibrillation may not be accompanied by obvious symptoms or a noticeable decrease in quality life. Such asymptomatic atrial fibrillation is usually diagnosed by chance during an examination and can be considered a clinical finding. According to a number of studies, every third to fifth patient with atrial fibrillation was asymptomatic, and in a recently completed study in patients with paroxysmal atrial fibrillation, more than 50% of all episodes of arrhythmia were asymptomatic. When newly diagnosed atrial fibrillation, the asymptomatic form may occur in 83.2% of cases [1].The purpose of the research is on the basis of studying the clinical features of the course of atrial fibrillation, determine the effect of asymptomatic arrhythmia on the development of fatal complications and patient survival in various forms of atrial fibrillation and develop a therapeutic strategy for managing patients with asymptomatic atrial fibrillation for the first time.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".