Frailty syndrome in patients with heart rhythm disorders
Bibliographic record
Abstract
AIM: To assess the prevalence of frailty syndrome in patients with heart rhythm disorders that qualified for pacemaker implantation. METHODS: The study included 171 patients (83 women, aged 73.9 ± 6.7 years) who qualified for pacemaker implantation as a result of sinus node dysfunction (81 patients) or atrio-ventricular blocks (AVB; 90 patients). A total of 60 patients (25 women, aged 72.40 ± 7.09 years) without heart rhythm disorders were included in the control group. Frailty syndrome was diagnosed using the Canadian Study of Health and Aging Clinical Frailty Scale test. RESULTS: Frailty syndrome was diagnosed in 25.15% of the patients, and pre-frailty in 36.84% of the patients. Frailty syndrome was diagnosed in 10% of the control group, and the average value of frailty was 3.35 ± 0.92. Frailty occurred significantly more often among patients with AVB (33.34%) compared with patients who were diagnosed with sinus node dysfunction (16.05%); P = 0.0081. The average score of frailty for sinus node dysfunction was 3.71 ± 0.89, and for AVB it was 4.14 ± 0.93; P = 0.0152. In the case of AVB, the women had a statistically more intense level of frailty of 4.54 ± 0.90 as compared with the men 3.87 ± 0.85; P = 0.0294. In the multiple logistic analysis, the presence of any arrhythmia was strongly associated with frailty syndrome (OR 2.1286, 95% CI 1.4594 - 3.1049; P = 0.0001). CONCLUSIONS: Frailty syndrome was diagnosed in one-quarter of patients with cardiac arrhythmias, whereas a further 40% were at a higher risk of frailty syndrome, and its occurrence was significantly higher if compared with the control group. Frailty occurred significantly more often among patients with atrio-ventricular blocks, especially in women. The results of the present research showed that there is a statistical association between frailty and arrhythmias. Geriatr Gerontol Int 2017; 17: 1313-1318.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".