The trajectory of changes in cognitive frailty and factors influencing it in elderly patients with cerebral infarction
Bibliographic record
Abstract
[Objective] The trajectory of cognitive frailty in elderly patients with cerebral infarction was analyzed, and the influencing factors were discussed. [ Methods] A total of 100 elderly patients with cerebral infarction hospitalized in Suzhou Hospital of Anhui Medical University from January to September in 2023 were selected for the study.Clinical data were collected on the patients, and cognitive frailty was assessed using the Frailty Screening Scale and the Montreal Cognitive Assessment Questionnaire (MoCA) at the time of admission and 1, 3, 6 months after treatment.Failty score ≥3 scores and MoCA <26 scores were defined as cognitive fraily.Potential categories of cognitive frailty change trajectories were identified by latent category growth modeling, and multifactorial logistic regression analyzed the factors influencing patients' cognitive frailty change trajectories. [ Results] The trajectory of cognitive frailty change in elderly patients with cerebral infarction identified by model fitting can be categorized into a group with a persistently low level of frailty (30 cases), a group with a slow rise in frailty (52 cases), and a group with a persistently high level of frailty (18 cases).Multifactorial logistic regression analysis showed that older age, poor sleep quality, and the number of comorbid chronic diseases > 4 were all risk factors for the development of persistent low levels of cognitive frailty into a slow rise in cognitive decline in elderly patients with cerebral infarction (P<0.05), Higher body mass index and high self-care ability were both protective factors for the development of persistent low levels of cognitive decline into a slow rise in cognitive decline in elderly patients with cerebral infarction (P<0.05).Older age, poor sleep quality and the number of comorbid chronic diseases >4 were risk factors for the progression of persistent low levels of cognitive decline to persistent high levels of cognitive decline in elderly patients with cerebral infarction (P < 0.05), and higher self-care ability were protective factors for the progression of persistent low levels of cognitive decline to persistent high levels of cognitive decline in elderly patients with cerebral infarction (P < 0.05). [Conclusion] Elderly patients with cerebral infarction can be divided into three trajectory types of cognitive decline changes, and there is group heterogeneity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".