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Record W4294925621 · doi:10.1111/cge.14224

Genetic overview of postaxial polydactyly: Updated classification

2022· review· en· W4294925621 on OpenAlexaff
Zaheer Ahmad, Romana Liaqat, Oliva Palander, Muhammad Bilal, Shah Zeb, Farooq Ahmad, Muhammad Jawad Khan, Muhammad Umair

Bibliographic record

VenueClinical Genetics · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital limb and hand anomalies
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsPolydactylyGLI3Limb developmentGeneticsBiologyGene

Abstract

fetched live from OpenAlex

Polydactyly or polydactylism, also known as a hyperdactyly, is a congenital limb defect with various morphologic phenotypes. Apart from physical and functional impairments, the presence of polydactyly is an indication of an underlying syndrome in the newborn. Usually, it follows as an autosomal dominant/recessive inheritance pattern with defects in the limb development's anteroposterior patterning. Although mutations in several genes have been associated with polydactyly; however, the exact underlying cause, pathways, and disease mechanisms are still unexplored, thus making it of multi-factorial origin. Polydactyly is divided into three subtypes; radial, ulnar, and central polydactyly. So far, 11 loci (PAPA1-PAPA11) and seven human genes have been reported to cause non-syndromic postaxial polydactyly in humans, including the ZNF141, GLI3, IQCE, GLI1, FAM92A1, KIAA0825, and DACH1. In this review, we discuss emerging evidences of clinical and molecular characterization of polydactyly types in term of the involvement of newly associated genes and loci for non-syndromic postaxial polydactyly, and how these might impact our understanding of the genetic mechanisms and molecular etiology involved in the cause of polydactyly.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.245
GPT teacher head0.443
Teacher spread0.198 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations25
Published2022
Admission routes1
Has abstractyes

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