KBG Syndrome: Prospective Videoconferencing and Use of AI-driven Facial Phenotyping in 25 New Patients
Bibliographic record
Abstract
Abstract Genetic variants in the gene Ankyrin Repeat Domain 11 ( ANKRD11 ) and deletions in 16q24.3 are known to cause KBG syndrome, a rare syndrome associated with craniofacial, intellectual, and neurobehavioral anomalies. We report 25 unpublished individuals from 22 families, all with molecularly confirmed diagnoses of KBG syndrome. Twenty-one individuals have de novo variants, three have inherited variants, and one is inherited from a parent exhibiting low-level mosaicism. Of the variants, 20 are truncating (frameshift or nonsense), and the remaining five individuals have missense variants (with one of these in three family members). One of the missense variants has been found in at least two other affected individuals. We created a novel protocol for collection and reporting of data, including prospectively interviewing these individuals and their families throughout eight countries via videoconferencing by a single clinician. Participants’ medical records, including imaging, were reviewed, and data was uploaded to the Human Disease Gene website using Human Phenotype Ontology (HPO) terms. Photos of the participants were submitted to GestaltMatcher and Face2Gene (FDNA Inc, USA) for facial analysis, and we found similar facial phenotypes among the participants. Within our cohort, common traits included short stature, macrodontia, anteverted nares, wide nasal bridge, wide nasal base, thick eyebrows, synophrys and hypertelorism. Seventy-two percent of participants had gastrointestinal complaints and 80% had hearing loss. Three participants were started on growth hormone with positive results. Behavioral issues and global developmental delays were found in most participants. Neurologic abnormalities including seizures and/or EEG abnormalities were also very common (44%), suggesting that early detection and seizure prophylaxis could be an important point of intervention. Twenty-four percent were diagnosed with attention deficit hyperactivity disorder (ADHD) and 28% were diagnosed with autism spectrum disorder (ASD). Additionally, we have identified minimally reported symptoms, including recurrent sinus infections (16%) and previously unreported migraines (20%). Based on the videoconferencing and these data, we provide a set of recommendations regarding diagnostic and treatment approaches for KBG syndrome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".