Cardiac arrhythmias in geriatric patients receiving palliative care support
Bibliographic record
Abstract
Introduction: Although palliative care patients often undergo electrocardiography (ECG), a detailed cardiac examination is often skipped.The aim of this study was to determine the incidence of arrhythmia in older patients in need of palliative care and to evaluate risk factors for asymptomatic arrhythmias. Material and methods:This prospective observational study was conducted between 1 March and 1 September 2022 among inpatients in the palliative care unit of Atatürk University Faculty of Medicine Hospital.Malnutrition status was assessed using the full Mini Nutritional Assessment.Delirium was assessed at hospitalization using the confusion assessment method.Electrocardiography was performed in all patients at admission to the palliative care unit.This was followed by 12-lead, 24-hour ambulatory ECG to detect arrythmias.Electrocardiography recordings were evaluated.Results: The 100 patients included in the study had a median age of 78 years, and 63.0% were women.Arrythmias were detected on Holter ECG in 70 patients (70.0%).The most common were premature ventricular contraction (PVC) (56.5%) and atrial fibrillation (AF) (30.4%).There was a statistically significant negative moderate correlation between PVC load and left ventricular ejection fraction (r = -0.308;p = 0.002).Premature ventricular contraction load was significantly higher in men than in women (p = 0.018).Of the patients with AF, 17 (17.0%)were under anticoagulant therapy.Left ventricular ejection fraction differed significantly according to the presence of AF and anticoagulant use.Left ventricular ejection fraction was lower in patients with AF and anticoagulant use compared to those without AF (p = 0.001). Conclusions:The prevalence of arrhythmias in palliative care patients is considerable.The treatment of arrhythmias in this patient population is complicated by polypharmacy, comorbidities, and frailty.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".