Understanding the Learning Disabilities Linked to Sagittal Craniosynostosis
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to investigate further findings that corroborate similarities between corrected sagittal craniosynostosis and attention deficit hyperactivity disorder (ADHD). The aim is to further characterize the neurocognitive deficits seen in adolescents with corrected craniosynostosis by comparing it to established learning deficits such as ADHD. METHODS: A total of 30 functional magnetic resonance imaging (fMRI) of 10 sagittal nonsyndromic craniosynostosis (sNSC), 10 ADHD-combined, and 10 control adolescents were studied. The fMRI scans were analyzed utilizing Statistical Parametric Mapping (University College London, UK) and analyzed with BioImageSuite (Yale University, New Haven, CT). RESULTS: The ADHD has lower connectivity to Brodmann area (BA) 11 (Montreal Neurological Institution [MNI]: -12,26,-21), BA20 (MNI: 62,-24,-25), and BA21 (MNI: 62,-32,-23) compared to sNSC and controls (P < 0.001). The sNSC has a unique visuospatial defect, compared to ADHD, created by decreased connectivity to BA31 (MNI: -3,-68,37), BA7 (MNI: -4,-68,41), BA19 (MNI: 0,-83,31), visual association cortex (MNI: -4,-78,22), and primary visual cortex (MNI: 7,-74,21) (P < 0.001). CONCLUSION: Patients born with sNSC have different neural connections than children born with ADHD. Patients born with sNSC have decreased connections in areas of visual processing and increased connections in areas of attention and auditory processing than patients with ADHD. Therefore, children with sagittal craniosynsotosis may have learning difficulties that, similar, yet different from ADHD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".