Association between pre-stroke frailty status and post-stroke cognitive impairment in patients with acute large artery atherosclerotic cerebral infarction
Bibliographic record
Abstract
The aim of this study was to investigate the correlation between pre-stroke frailty status and post-stroke cognitive impairment (PSCI) in patients with acute large artery atherosclerotic cerebral infarction. One hundred and eight patients with acute large artery atherosclerotic cerebral infarction admitted in our hospital from July 2020 to July 2023 were prospectively enrolled. Patients were stratified into frailty (46 cases) and non-frailty groups (62 cases) based on FRAIL scale scores. During the 6-month follow-up after the onset of cerebral infarction, patients were evaluated using the Chinese modified version of Montreal Cognitive Assessment (MoCA) scale for cognitive function and were divided into PSCI (52 cases) and non-PSCI (56 cases) groups. The frailty group showed significantly higher age, prevalence of hypertension and diabetes comorbidities, smoking and alcohol consumption rates, National Institutes of Health Stroke Scale (NHISS) score, and Modified Rankin Scale (mRS) score than those in the non-frailty group (P<0.05, P<0.01). The incidence of PSCI in the frailty group was also significantly higher than that in the non-frailty group (78.3 vs 25.8%, P<0.01). Compared to the non-PSCI group, the PSCI group had higher age, shorter education duration, fewer cases of reperfusion therapy, and greater frailty (P<0.05, P<0.01). Logistic regression analysis showed that pre-stroke frailty was an independent risk factor for PSCI (P<0.01). Timely assessment of the frailty status in patients with acute large artery atherosclerotic cerebral infarction is beneficial for preventing, delaying onset, and reducing the incidence of PSCI.
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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 | 0.001 |
| 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".