Association between HDL levels and stroke outcomes in the Arab population
Bibliographic record
Abstract
Low HDL levels are associated with an increased stroke incidence and worsened long-term outcomes. The aim of this study was to assess the relationship between HDL levels and long-term stroke outcomes in the Arab population. Patients admitted to the Qatar Stroke Database between 2014 and 2022 were included in the study and stratified into sex-specific HDL quartiles. Long-term outcomes included 90-Day modified Rankin Score (mRS), stroke recurrence, and post-stroke cardiovascular complications within 1 year of discharge. Multivariate binary logistic regression analyses were performed to identify the independent effect of HDL levels on short- and long-term outcomes. On multivariate binary logistic regression analyses, 1-year stroke recurrence was 2.24 times higher (p = 0.034) and MACE was 1.99 times higher (p = 0.009) in the low-HDL compared to the high-HDL group. Mortality at 1 year was 2.27-fold in the low-normal HDL group compared to the reference group (p = 0.049). Lower sex-specific HDL levels were independently associated with higher adjusted odds of 1-year post-stroke mortality, stroke recurrence, and MACE (p < 0.05). In patients who suffer a stroke, low HDL levels are associated with a higher risk of subsequent vascular complication.
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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 | 0.001 |
| 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 | 0.001 | 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".