Symptom network of multidomain frailty in patients with chronic heart failure: a network-based analysis
Bibliographic record
Abstract
AIMS: The purpose of this study was to construct a symptom network for identifying core symptoms of frailty in physical, cognitive, and social domains in patients with chronic heart failure (CHF). METHODS AND RESULTS: A total of 269 hospitalized patients with CHF was included in the study. The FRAIL scale, the Montreal Cognitive Assessment (MoCA), and the Help, Participation, Loneliness, Financial, Talk scale (The HALFT scale) were used to assess the frailty in physical, cognitive, and social domains. The construction of symptom networks for frailty in physical, cognitive, and social domains in patients with CHF was conducted by R software. The symptom network analysis indicated that the three symptoms with the highest centrality of strength were 'attention' (rs = 4.178), 'visuospatial/executive function' (rs = 3.940), and 'language' (rs = 3.843). The three symptoms with the highest centrality of betweenness were 'language' (rb = 32.000), 'loneliness' (rb = 28.000), and 'decreased endurance' (rb = 26.000). The three symptoms with the highest centrality of closeness were 'attention' (rc = 0.011621), 'visuospatial/executive function' (rc = 0.011620), and 'language' (rc = 0.011568). The three symptoms with the highest centrality of bridge were 'decreased walking ability' (rbs = 7.608), 'decreased endurance' (rbs = 7.373), and 'inability to help others' (rbs = 4.990). CONCLUSION: The core symptoms of frailty in the physical, cognitive, and social domains among patients with CHF are attention, visuospatial/executive function, and language function. Therefore, prioritizing interventions for core symptoms should be implemented to address frailty in the physical, cognitive, and social domains of patients with CHF in clinical practice.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".