Abstract Number ‐ 262: Cognitive Decline in Patients with Symptomatic Intracranial Stenosis and Effect of Serum Lipid Profile
Bibliographic record
Abstract
Introduction We determined the effect of baseline low density lipoprotein (LDL) and triglyceride (TG) concentrations on cognitive decline, in patients with symptomatic high grade intracranial stenosis. Methods We analyzed a cohort of stroke patients who had baseline and 4 months follow‐up Montreal Cognitive Assessment (MoCA) scores. MoCA score worsening after 4 months was the outcome of interest, defined as worsening of ≥ 2 points from baseline. Baseline LDL and TG concentrations (mg/dl) were assessed, and univariate and multivariate analysis using logistic regression were performed to identify predictors of worsening MoCA scores. Results A total of 349 patients were analyzed. 313 and 315 patients had baseline LDL and TG, respectively. 61 (17%) had a worsening of MoCA scores. The mean baseline LDL was 97.3 (39.1) in the worsening group, compared to 110.6 (44.8) in those with no worsening (p = 0.014). 23% of patients with LDL < 100 had worsening in MoCA score compared to 11% of patients with LDL ≥ 100 (p< 0.006). The mean baseline TG was 145.7 (102.2) in the worsening group, compared to 170.7 (135.4) (p = 0.053). On multivariate analysis, higher age (p = 0.014) and lower baseline LDL (p = 0.046) were predictors of worsening MoCA scores. Conclusions Patients who did not undergo any worsening MoCA scores had higher LDL and TG levels at baseline, and only older age and lower baseline LDL scores predicted worsening scores at 4 months. Presumably, the patients with high baseline lipid levels were treated more aggressively with dietary modifications and statins, which could have led to cognitive protective effect regardless of serum lipid profile.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".