Dubowitz Syndrome: A Review and Implications for Cognitive, Behavioral, and Psychological Features
Bibliographic record
Abstract
UNLABELLED: Dubowitz syndrome is a rare autosomal recessive disorder characterized by micorcephaly, short stature, abnormal faces, and mild to severe mental retardation. Growth retardation occurs both intrauterine and postnatal. Behavioral characteristics include hyperactivity, short attention span, and aggressiveness. Behavior problems include difficulty feeding, sleep disturbance, and bedwetting. Individuals with the disorder have displayed shyness, fear of crowds, and dislike of loud noises. A high-pitched or hoarse voice is common. Deficits have been found in speech and language skills, reasoning and memory skills, self-help skills, and psychomotor functioning. Ocular, dental, cutaneous, skeletal, cardiovascular, gastrointestinal, neurological, immunological, and hematological medical difficulties have been noted. Approximately 148 cases have been described in the literature. The cause of the disorder remains unknown, however, research suggests genetic origin. Past research emphasizes physical characteristics and medical complications. There is a lack of cognitive, behavioral, and psychological information available regarding the disorder. This article presents a review of the literature and provides assessment and treatment implications for the cognitive, behavioral, and psychological aspects of Dubowitz syndrome. KEYWORDS: Dubowitz; Syndrome; Autosomal; Recessive.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".