Assessment of frailty status in patients with acute cerebral infarction and their relationship with serum markers
Bibliographic record
Abstract
OBJECTIVE: Frailty status is closely related to cerebral infarction, but there is a lack of objective biomarkers to determine frailty status in cerebral infarction patients. This study explores frailty status and frailty-related serum markers in patients with acute cerebral infarction and determines their diagnostic value for frailty. METHODS: A total of 146 patients with acute cerebral infarction admitted to Hangzhou Third people's Hospital from January 2021 to December 2023 were enrolled prospectively. The Edmonton scale was used to evaluate the patients in the frailty and non-frailty groups. The clinical frailty scale (CFS) was used to divide frailty patients into mild, moderate, and severe frailty groups, comparing clinical data and levels of serum markers among different groups, and analyzing the risk factors for frailty in cerebral infarction. RESULTS: Among the 146 patients, 70 cases (47.9%) were in the frailty group, and 76 cases (52.1%) in the non-frailty group. Compared with patients in the non-frailty group, patients in the frailty group had significantly lower levels of hemoglobin, triglycerides, low-density lipoprotein, and albumin (P<0.05 or 0.01), while levels of C-reactive protein (CRP), D-dimer, and homocysteine (Hcy) were significantly increased (P<0.05 or 0.01). Logistic regression analysis found that the levels of hemoglobin and Hcy were independent risk factors for frailty in acute cerebral infarction patients, with the ROC curve areas of 0.707 and 0.751, respectively. The ROC curve area for predicting frailty by combining hemoglobin and Hcy levels was 0.799. CONCLUSION: The incidence of frailty in patients with acute cerebral infarction is high, and serum markers of hemoglobin and Hcy have certain value in determining frailty in patients with acute cerebral infarction.
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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 | 0.001 | 0.003 |
| 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".