Does Fasting Triglyceride Level Influence Core Infarct Volume in Acute Stroke?
Bibliographic record
Abstract
OBJECTIVE: Our study explores the relationship between fasting triglyceride levels and initial infarct volume in acute ischemic stroke (AIS) patients. METHODS: We performed a retrospective chart review and cross-sectional analysis of AIS patients admitted to a tertiary care center in Kansas from 2010 to 2023. Inclusion criteria were AIS patients who (1) underwent CTA and CTP within 24 hours of stroke onset, and (2) had fasting triglyceride levels measured within 24 hours of admission. Infarct volume was calculated using RAPID software (iSchemaView Inc.). Statistical analyses were conducted using STATA (Release 16), with T tests, ANOVA, χ 2 tests, and multivariable linear regression applied as appropriate. RESULTS: We included 178 patients, 52% (n=92) of whom were male, and 31% were aged 61 to 70 years. Mean TG levels were 116.91±70.23 mg/dL, and mean infarct volume was 41.64±53.35 mL. Linear regression showed a significant positive association between TG levels and infarct volume ( P <0.01, β=0.17, 95% CI: 0.06-0.28), with a 0.17 mL increase in infarct volume per unit increase in TG levels. Patients with Embolic stroke of undetermined source (ESUS) had larger infarct volumes compared with those with large artery atherosclerosis ( P <0.05) and the highest mean TG levels (135.61 mg/dL). CONCLUSION: Hypertriglyceridemia was positively associated with larger infarct volumes, particularly in ESUS patients, who had the highest TG levels and larger infarct sizes. These findings suggest that elevated TG may predict worse stroke outcomes and could be a potential therapeutic target for stroke prevention.
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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 | 0.000 |
| 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".