Associated risk factors for silent cerebral infarcts in sickle cell anemia: low baseline hemoglobin, sex, and relative high systolic blood pressure
Bibliographic record
Abstract
The most common form of neurologic injury in sickle cell anemia (SCA) is silent cerebral infarction (SCI). In the Silent Cerebral Infarct Multi-Center Clinical Trial, we sought to identify risk factors associated with SCI. In this cross-sectional study, we evaluated the clinical history and baseline laboratory values and performed magnetic resonance imaging of the brain in participants with SCA (HbSS or HbSβ° thalassemia) between the ages of 5 and 15 years with no history of overt stroke or seizures. Neuroradiology and neurology committees adjudicated the presence of SCI. SCIs were diagnosed in 30.8% (251 of 814) participants who completed all evaluations and had valid data on all prespecified demographic and clinical covariates. The mean age of the participants was 9.1 years, with 413 males (50.7%). In a multivariable logistic regression analysis, lower baseline hemoglobin concentration (P < .001), higher baseline systolic blood pressure (P = .018), and male sex (P = .030) were statistically significantly associated with an increased risk of an SCI. Hemoglobin concentration and systolic blood pressure are risk factors for SCI in children with SCA and may be therapeutic targets for decreasing the risk of SCI. This study is registered at www.clinicaltrials.gov as #NCT00072761.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".