Prevalence and factors associated with cognitive frailty in heart failure: a systematic review and meta-analysis protocol
Bibliographic record
Abstract
INTRODUCTION: Heart failure (HF) is a global health issue affecting millions of people worldwide. Cognitive frailty, a syndrome characterised by physical frailty and cognitive impairment without dementia, is increasingly recognised in this population. Cognitive frailty is associated with worse outcomes, including increased hospitalisation, disability and mortality. This systematic review and meta-analysis aimed to investigate the incidence, prevalence and predictors of cognitive frailty in HF patients. METHODS: A systematic search will be conducted in MEDLINE/PubMed, EMBASE/Ovid, Web of Science and Google Scholar from inception to the latest search date. Eligible studies will report original data on adult patients (age ≥18 years) with HF, focusing on the incidence, prevalence and predictors of cognitive frailty. Two investigators will independently extract data and assess study quality using the Newcastle-Ottawa Scale and mixed-methods appraisal tool. Meta-analyses and meta-regression will be performed to estimate the pooled prevalence of cognitive frailty in HF patients and to identify predictors associated with increased risk, respectively. Subgroup analyses will be conducted to explore potential sources of heterogeneity. ETHICS AND DISSEMINATION: This systematic review does not require ethical approval and informed consent, as it does not use identifiable patient data. The results of this study will be submitted for publication in a peer-reviewed medical journal. This comprehensive meta-analysis of the literature on cognitive frailty among HF patients will inform tailored interventions and management strategies, ultimately improving patients' quality of life and outcomes.
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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 | 0.054 | 0.063 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.027 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.059 | 0.006 |
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".