Factors Influencing Cognitive Impairment in Patients Undergoing Hemodialysis: Based on Health Ecological Model
Bibliographic record
Abstract
INTRODUCTION: Cognitive impairment represents a prevalent issue among patients undergoing hemodialysis (HD). Nevertheless, the majority of existing studies have predominantly focused on its influencing factors from a single-dimensional perspective. This study aimed to comprehensively investigate the multifaceted factors contributing to cognitive impairment in patients undergoing HD by applying the health ecological model. METHOD: Data of 172 patients undergoing HD from the HD unit from China were collected from June 2022 to October 2022. A total of 24 variables were collected across the five dimensions of the health ecological model. The Montreal Cognitive Assessment (MoCA) scale was used to assess cognitive function. LASSO regression was utilized to select relevant variables, and binary logistic regression was employed to determine the independent risk factors associated with cognitive impairment. RESULTS: Gender, age, protein intake, and cholesterol were independently associated with cognitive impairment in patients undergoing HD. The predictive model incorporating these factors achieved a moderate goodness-of-fit (Nagelkerke R2 = 0.543), reflecting their combined contribution to cognitive risk. CONCLUSION: Cognitive impairment is highly prevalent among Chinese patients undergoing HD, influenced by factors such as aging, female sex, insufficient protein intake, and low cholesterol levels. These findings underscore the multifactorial nature of cognitive decline in patients undergoing HD within the framework of a health ecological model, highlighting the need for comprehensive interventions that address biological, behavioral, psychological, disease-related, and social domains.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".