Cognitive Correlates of Math Performance in School-Aged Children with Sickle Cell Disease and Silent Cerebral Infarcts
Bibliographic record
Abstract
OBJECTIVE: Attention, processing speed, executive functioning, and math difficulties are common in youth with sickle cell disease (SCD) with silent cerebral infarcts (SCI). This study investigated the cognitive underpinnings of math difficulties in children with SCD and SCI. METHOD: Youth (n = 68) with SCD and SCI completed measures of attention [Digit Span forward (DSF); Conners Continuous Performance Test-Third Edition/Kiddie Conners Continuous Performance Test-Second Edition (CPT-3/KCPT-2)]; working memory [Wechsler Intelligence Scales (WPPSI-IV, WISC-IV, WISC-V, WAIS-IV), Working Memory Index (WMI), Digit Span backwards (DSB)]; processing speed [WPPSI-IV, WISC-IV, WISC-V, WAIS-IV Processing Speed Index (PSI)]; math reasoning [Wechsler Individual Achievement Test-Third Edition (WIAT-III) Mathematics composite (MC)]; and math fluency [WIAT-III Math Fluency composite (MF)] as part of a clinical neuropsychological evaluation. Parent ratings of attention and executive functioning were obtained [Behavior Assessment System for Children-Third Edition (BASC-3), Behavior Rating Inventory of Executive Function (BRIEF)]. RESULTS: MC was positively correlated with WMI (r = 0.59, p = 0.00), PSI (r = 0.40, p < 0.001), DSF (r = 0.29, p = 0.03), DSB (r = 0.47, p < 0.001), and MF (r = 0.71, p < 0.001). Correlations between MC, sustained attention, and parent ratings were nonsignificant. The linear regression model using correlated variables was significant [F(4,51) = 8.29, R2 = 0.39, p < 0.001]. WMI was the only significant variable within the model (p = 0.02). CONCLUSIONS: Working memory deficits account for significant variance in untimed mathematical performance in this population-consistent with other populations with white matter dysfunction. Interventions targeting both mathematics and working memory may be beneficial.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".