Silent cerebral infarction, income, and grade retention among students with sickle cell anemia
Bibliographic record
Abstract
Children with sickle cell anemia have a higher-than-expected prevalence of poor educational attainment. We test two key hypotheses about educational attainment among students with sickle cell anemia, as measured by grade retention and use of special education services: (1) lower household per capita income is associated with lower educational attainment; (2) the presence of a silent cerebral infarct is associated with lower educational attainment. We conducted a multicenter, cross-sectional study of cases from 22 U.S. sites included in the Silent Infarct Transfusion Trial. During screening, parents completed a questionnaire that included sociodemographic information and details of their child's academic status. Of 835 students, 670 were evaluable; 536 had data on all covariates and were used for analysis. The students' mean age was 9.4 years (range: 5-15) with 52.2% male; 17.5% of students were retained one grade level and 18.3% received special education services. A multiple variable logistic regression model identified that lower household per capita income (odds ratio [OR] of quartile 1 = 6.36, OR of quartile 2 = 4.7, OR of quartile 3 = 3.87; P = 0.001 for linear trend), age (OR = 1.3; P < 0.001), and male gender (OR, 2.2; P = 0.001) were associated with grade retention; silent cerebral infarct (P = 0.31) and painful episodes (P = 0.60) were not. Among students with sickle cell anemia, household per capita income is associated with grade retention, whereas the presence of a silent cerebral infarct is not. Future educational interventions will need to address both the medical and socioeconomic issues that affect students with sickle cell anemia.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".