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Record W2608904569 · doi:10.1002/ajmg.a.38241

Variable developmental delays and characteristic facial features—A novel 7p22.3p22.2 microdeletion syndrome?

2017· article· en· W2608904569 on OpenAlexaff
Andrea C. Yu, Regina M. Zambrano, Ingrid Cristian, Sue Price, Birgitta Bernhard, Marc Zucker, Sunita Venkateswaran, Jean McGowan‐Jordan, Christine M. Armour

Bibliographic record

VenueAmerican Journal of Medical Genetics Part A · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomic variations and chromosomal abnormalities
Canadian institutionsUniversity of OttawaChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsMilestonePediatricsMicrocephalyMedicine

Abstract

fetched live from OpenAlex

Isolated 7p22.3p22.2 deletions are rarely described with only two reports in the literature. Most other reported cases either involve a much larger region of the 7p arm or have an additional copy number variation. Here, we report five patients with overlapping microdeletions at 7p22.3p22.2. The patients presented with variable developmental delays, exhibiting relative weaknesses in expressive language skills and relative strengths in gross, and fine motor skills. The most consistent facial features seen in these patients included a broad nasal root, a prominent forehead a prominent glabella and arched eyebrows. Additional variable features amongst the patients included microcephaly, metopic ridging or craniosynostosis, cleft palate, cardiac defects, and mild hypotonia. Although the patients' deletions varied in size, there was a 0.47 Mb region of overlap which contained 7 OMIM genes: EIP3B, CHST12, LFNG, BRAT1, TTYH3, AMZ1, and GNA12. We propose that monosomy of this region represents a novel microdeletion syndrome. We recommend that individuals with 7p22.3p22.2 deletions should receive a developmental assessment and a thorough cardiac exam, with consideration of an echocardiogram, as part of their initial evaluation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.668
Threshold uncertainty score0.548

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.242
Teacher spread0.233 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations13
Published2017
Admission routes1
Has abstractyes

Explore more

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