Impact of triglyceride playing on stroke severity correlated to bilirubin
Bibliographic record
Abstract
Major lipids making effects on the occurrence of acute ischemic stroke (AIS) is well recognized, but their roles on stroke severity remain uncertain. To explore the exact roles of lipids playing on stroke severity and the possible mechanism, we conduct this observational study.Data was collected from patients with AIS from February 2008 to May 2012. The level of major lipids was compared among AIS groups with different severity and investigated the correlation. Also, the relationship existed between major lipids and bilirubin. Mechanism of major lipids playing on stroke severity was researched to determine if oxidative stress reflected by bilirubin.Lower triglyceride (TG) and higher high density lipoprotein cholesterol (HDL-C) were observed in severe stroke, and obvious correlation existed between TG and stroke severity or HDL-C and stroke severity. TG was associated negatively with direct bilirubin (DBIL) and total bilirubin (TBIL), and lower level of DBIL and TBIL were related to higher quartiles of TG. There was no obvious difference of DBIL and TBIL among the groups of quartiles of HDL-C. TG was the influence factor of stroke severity in severe stroke through multiple univariable logistic regression. But it was not the independent influence factor after multivariable logistic regression adjusted by DBIL or TBIL. However, HDL-C was the influence factor of stroke severity through both univariable and multivariable logistic regression.Lower TG or higher HDL-C predicted severer stroke. The effect of TG on stroke severity was mediated by bilirubin, not HDL-C.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".