Role of Neurocognitive Factors in Academic Fluency for Children and Adults With Spina Bifida Myelomeningocele
Bibliographic record
Abstract
OBJECTIVES: Fluency is a major problem for individuals with neurodevelopmental disorders, including fluency deficits for academic skills. The aim of this study was to determine neurocognitive predictors of academic fluency within and across domains of reading, writing, and math, in children and adults, with and without spina bifida. In addition to group differences, we expected some neurocognitive predictors (reaction time, inattention) to have similar effects for each academic fluency outcome, and others (dexterity, vocabulary, nonverbal reasoning) to have differential effects across outcomes. METHODS: Neurocognitive predictors were reaction time, inattention, dexterity, vocabulary, and nonverbal reasoning; other factors included group (individuals with spina bifida, n=180; and without, n=81), age, and demographic and untimed academic content skill covariates. Univariate and multivariate regressions evaluated hypotheses. RESULTS: Univariate regressions were significant and robust (R 2 =.78, .70, .73, for reading, writing, and math fluency, respectively), with consistent effects of covariates, age, reaction time, and vocabulary; group and group moderation showed small effect sizes (<2%). Multivariate contrasts showed differential prediction across academic fluency outcomes for reaction time and vocabulary. CONCLUSIONS: The novelty of the present work is determining neurocognitive predictors for an important outcome (academic fluency), within and across fluency domains, across population (spina bifida versus typical), over a large developmental span, in the context of well-known covariates. Results offer insight into similarities and differences regarding prediction of different domains of academic fluency, with implications for addressing academic weakness in spina bifida, and for evaluating similar questions in other neurodevelopmental disorders. (JINS, 2019, 25, 249-265).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".