Prognostic Value of Plasma Lysophosphatidic Acid and CD62P in the Outcome of Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Stroke is the second leading cause of death worldwide, and only 10% of the patients can live their normal lives. As per the Global Burden of Disease Study 2016, the incidence of stroke in India is reported to be 1,175,778 (1 076 048 to 1,274,427) per 100000 person-years. A novel biochemical marker that can predict the stroke outcome can reduce morbidity, especially when thrombolysis cannot be done. Hence, the current study aims to study the role of plasma lysophosphatidic acid (LPA) and CD62P as prognostic markers for acute ischemic stroke. METHODS: This is a prospective observational study. Ninety-six patients who met the inclusion criteria were enrolled. The plasma LPA and CD62P levels were estimated using the enzyme-linked immunosorbent assay and correlated with the Alberta stroke program early computed tomography score (ASPECTS) on CT brain and modified Rankin scale (mRS) at 90 days. RESULTS: Age, sex, and other risk factors in the ischemic stroke patients did not have any impact on the levels of LPA and CD62P. Both LPA ( P = 0.000) and CD62P ( P = 0.005) showed a positive correlation with mRS after 90 days. The mean LPA was highest for mRS 5 and least for mRS 1, whereas the mean CD62P levels did not correlate with increasing mRS. The ASPECTS showed a significant negative correlation only with LPA. CONCLUSIONS: High LPS and CD62P levels after stroke onset tend to be associated with poor mRS scores at 90 days.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".