Elevated Systolic Blood Pressure and Low Fetal Hemoglobin Are Risk Factors for Silent Cerebral Infarcts in Children with Sickle Cell Anemia.
Bibliographic record
Abstract
Abstract Abstract 262 Introduction: The most common cause of neurological injury in sickle cell anemia is silent cerebral infarcts (SCI). In the Silent Cerebral Infarct Multi-Center Clinical Trial (SIT Trial) cohort, we sought to identify risk factors associated with SCI. Patients and Methods: In this cross-sectional study, we evaluated the clinical history, baseline laboratory values and performed magnetic resonance imaging of the brain. For those children with SCI-like lesions, a pediatric neurologist examined the child and neuroradiology and neurology committees adjudicated the presence of SCI. Children between the ages of 5 and 15 years with hemoglobin SS or S-beta° thalassemia and no history of overt strokes or seizure were evaluated. Results: A total of 542 children were evaluated; 173 (31.9%) had SCI. The mean age of the children was 9.3 years, with 280 males (51.7%). In a multivariate logistic analysis, two covariates were significant: a single systolic blood pressure (SBP) obtained during a baseline well-visit, p = 0.015 and hemoglobin F (Hgb F) level obtained after three years of age, p = 0.038. Higher values of SBP and lower values of Hgb F increased the odds of SCI; Figure. Baseline values of white blood cell count, hemoglobin level, oxygen saturation, reticulocytes, pain, or ACS event rates were not associated with SCI. Conclusion: SBP and Hgb F level are two previously unidentified risk factors for SCI in children with sickle cell disease. Modulation of SBP and Hgb F levels might decrease the risk of SCI. Disclosures: No relevant conflicts of interest to declare.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| 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".