Investigation of cognitive function and analysis of related factors in patients with aneurysmal subarachnoid hemorrhage after receiving interventional treatment
Bibliographic record
Abstract
Objective To investigate the postoperative cognitive function in a group of patients with aneurysmal subarachnoid hemorrhage(SAH) who had received interventional therapy, and to analyze the related influence factors. Methods Montreal cognitive assessment(Mo CA) scale was used for dementia rating in 60 SAH patients, who had received interventional therapy, before they were discharged from hospital. Using multifactor logistic regression analysis method, the degree of cognitive impairment was evaluated according to the following three factors : patient's baseline data, disease condition and procedure outcome. The influence of each factor on the cognitive function was assessed. Results The results of Mo CA scale scores indicated that normal cognitive function(26 points) was seen in 20 patients, mild cognitive impairment(14-26 points) in 38 patients, moderate cognitive impairment(9-14 points) in 2 patients and severe cognitive impairment(9 points) in none. Logistic multifactor regression analysis showed that the following three factors were the most important predictors for predicting cognitive function damage in SAH patients after receiving interventional therapy: the age(P =0.04; OR: 1.122, 95% CI: 1.004-1.254), the Fisher grade of bleeding volume(P=0.01; OR: 23.834, 95%CI: 2.059-275.871) and the operation time(P =0.002; OR: 2 893, 95% CI: 19.043-439 500). Conclusion Patients with aneurysmal subarachnoid hemorrhage have certain degree of cognitive impairment. For younger patients with less bleeding, shorter operation time is helpful in reducing the damage of cognitive function.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".