Exploring the Cognitive and Behavioral Aspects of Shprintzen‐Goldberg Syndrome; a Novel Cohort and Literature Review
Bibliographic record
Abstract
Shprintzen-Goldberg-syndrome (SGS) is caused by pathogenic exon 1 variants of SKI. Symptoms include dysmorphic features, skeletal and cardiovascular comorbidities, and cognitive and developmental impairments. We delineated the neurodevelopmental and behavioral features of SGS, as they are not well-documented. We collected physician-reported data of people with molecularly confirmed SGS through an international collaboration. We identified and deep-phenotyped the neurodevelopmental and behavioral features in four patients. Within our cohort, all exhibited developmental delays in motor skills and/or speech, with the average age of first words at 2 years and 6 months and independent walking at 3 years and 5 months. All four had learning disabilities and difficulties regulating emotions and behavior. Intellectual disability, ranging from borderline to moderate, was present in all four participants. Moreover, we reviewed the literature and identified 52 additional people with SGS, and summarized the features across both datasets. Mean age was 23 years (9-48 years). When combining our cohort and reported cases, we found that 80% (45/56) had developmental and/or cognitive impairment, with the remainder having normal intelligence. Our study elucidates the developmental, cognitive, and behavioral features in participants with SGS and contributes to a better understanding of this rare condition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".