ANALYSIS OF FACTORS ASSOCIATED WITH FRAILTY SYNDROME IN PATIENTS WITH HEART FAILURE
Bibliographic record
Abstract
OBJECTIVE: Aim: Determination of factors associated with frailty syndrome (FS) in patients with heart failure (HF). PATIENTS AND METHODS: Materials and methods: Consecutive patients hospitalized in the department were assessed for the presence of FS using L. Fried criteria, Edmonton Frail Scale (EFS) and Tilburg Frailty Indicator (TFI). Presence of arterial hypertension, diabetes, obesity, chronic obstructive pulmonary disease (COPD), and heart failure was included in the analysis based on patients' medical history and findings from current hospitalization. Patients were assessed for the presence of depression using Beck's Depression Inventory (BDI). Physical capacity was assessed using NYHA classification. RESULTS: Results: 87 patients (mean age 81.4±6.7; 57 women; 11 HFrEF, mean NYHA 2.36±1.21; 11 HFmrEF, mean NYHA 2.18±1.08; 65 HFpEF mean NYHA 1.94±1.09) were included in the analysis. Multivariable analysis showed significant relationship between FS assessed with EFS and age (β=0.316, SE=0.08; p=0.0001), arterial hypertension (β=-0.194, SE=0.08; p=0.0173), COPD (β=0.176, SE=0.08; p=0.0300) and depression (β=0.565, SE=0.08; p=0.0000). FS assessed with L. Fried criteria was significantly related to age (β=0.359, SE=0.09; p= 0.0001), NYHA classification (β= 0.336, SE=0.09; p=0.0002) and depression (β=0.297, SE=0.09; p=0.0010). Age (β=0.251, SE=0.10; p=0.0114) and depression (β=0.375, SE=0.1; p=0.0002) were significantly related to FS assessed using TFI. In multivariable analysis HF phenotype was not significantly related to FS. CONCLUSION: Conclusions: Age and depression assessed with BDI are related to FS in patients with HF. Arterial hypertension and COPD are linked to FS assessed using EFS, whereas NYHA classification is linked to FS assessed with L. Fried criteria. No statistically significant relationship was found between FS and HF phenotype.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".