Association Between Lipid Profile and Clinical Manifestations in Sickle Cell Anemia: A Systematic Review
Bibliographic record
Abstract
Abstract Introduction Sickle cell anemia (SCA) is a genetic disease associated with frequent episodes of acute illness. Changes in the lipid profile and a chronic inflammatory process make up the molecular aspects observed in this disease. Associations between these mechanisms and clinical manifestations could thus define severity profiles and therapeutic strategies. Objectives To verify whether there is an association between lipid profile and clinical manifestations in patients with SCA and if there is a correlation between lipid profile and laboratory markers in this disease. Methodology According to the PRISMA guidelines, a systematic review of the literature was conducted by searching the MEDLINE/PubMed, LILACS, SciELO, Scopus, and Cochrane databases. Articles were screened by reading the titles and abstracts, reaching those selected for full-text reading. The included studies were published between 2010 and 2020, were fully available in the databases, and addressed the proposed theme. The risk of individual bias was assessed by using the Joanna Briggs Institute checklist and the Newcastle-Ottawa scale. Results Out of the 144 identified articles, 15 were selected for analysis, resulting in a sample size of 2,230 individuals. HDL-C, LDL-C, total cholesterol , and triglycerides were the main variables analyzed in the lipid profiles. A correlation was observed between these variables and some of the most relevant clinical events in the disease, including vaso-occlusive seizures and acute thoracic syndrome. Conclusion Lipid metabolism disorders, especially hypocholesterolemia and hypertriglyceridemia, are linked to clinical events observed in SCA, suggesting they play a relevant role in the multifactorial pathogenesis of this disease.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".