Analysis of the trajectory of cognitive function changes and influencing factors in maintenance hemodialysis patients: a prospective longitudinal study
Bibliographic record
Abstract
OBJECTIVES: To explore the trajectory of cognitive function changes and influencing factors in maintenance hemodialysis (MHD) patients. METHODS: A convenience sampling method was used to select MHD patients from a tertiary hospital in Chengdu from August 2023 to April 2024. The general information questionnaire, Chinese version of the Montreal Cognitive Assessment (MoCA), Pittsburgh Sleep Quality Index (PSQI), Appetite Visual Analogue Scale (VAS), and Family Care Index (APGAR) were used for the investigation. Patients' cognitive function levels were assessed at baseline and at 3, 6, and 9 months after the initial survey. A latent growth model was used to identify potential categories of cognitive function trajectory, and univariate and binary logistic regression analyses were performed to analyze the influencing factors. RESULTS: A total of 154 MHD patients completed the entire study. The trajectory of cognitive function changes was divided into two potential categories: low cognitive function-fast decline group and high cognitive function-slow decline group. Binary logistic regression results showed that educational level, hypertension, sleep quality, appetite, and family care were influencing factors for the trajectory of cognitive function changes in MHD patients. CONCLUSIONS: Cognitive function in MHD patients showed an overall declining trend over time. The cognitive function change trajectory could be divided into two potential categories: fast decline group and high cognitive function-slow decline group. Healthcare professionals can develop targeted nursing intervention programs based on the characteristics of different patient types and their influencing factors to improve cognitive function and enhance quality of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".