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Record W3217649485 · doi:10.1101/2021.11.18.21266480

KBG Syndrome: Prospective Videoconferencing and Use of AI-driven Facial Phenotyping in 25 New Patients

2021· preprint· en· W3217649485 on OpenAlexaff
Lily Guo, Jiyeon Park, Edward Yi, Elaine Marchi, Tzung‐Chien Hsieh, Yana Kibalnyk, Yolanda Moreno-Sáez, Saskia Biskup, Oliver Puk, Carmela Beger, Anastassia Voronova, Peter Krawitz, Gholson J. Lyon

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsUniversity of Alberta
FundersOffice for People With Developmental Disabilities
KeywordsMissense mutationGeneticsIntellectual disabilityMedicineHypertelorismNonsenseFrameshift mutationPhenotypeBiologyGene

Abstract

fetched live from OpenAlex

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 We report 25 unpublished individuals from 22 families, all with molecularly confirmed diagnoses of KBG syndrome.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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.014
GPT teacher head0.239
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

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