Early language intervention and IQ of children with non-syndromic orofacial clefts
Bibliographic record
Abstract
Introduction Children with non-syndromic orofacial clefts are at higher risk for developmental difficulties. Speech and language as commonly affected developmental domains in these children. Objectives The aim of the current study was to explore the effects of early interventions for speech and language on later cognitive outcomes in this patient population. Methods A combined retrospective/prospective-comparative study was carried out at the Department of Pediatrics of the University of Pécs in Hungary. The participants were children between 6 and 16 years of age. The study consisted of a self-designed demographic questionnaire and an IQ test (WISC-IV). Results A total of 41 children with non-syndromic orofacial clefts and 44 age-matched controls participated in the study. Children of the cleft group were examined by pedagogical professional services and required special education plans significantly more often than controls (p<0.001 and p=0.02, respectively). Participants of the cleft group who received early speech and language therapy score higher on the Verbal Comprehension Index (p=.005). Full-Scale IQ score was also higher for cleft participants who received therapy, however not significant but borderline (p=0.08). Conclusions Early language and speech interventions for children with non-syndromic orofacial clefts may have a positive effect on verbal skills and overall cognitive development. Future longitudinal studies examining baseline cognitive functioning of infants are needed to provide more conclusive evidence on the effects of interventional programs on speech and language development in cleft patients. Disclosure of Interest None Declared
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".