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The impact of array genomic hybridization on mental retardation research: a review of current technologies and their clinical utility

2007· review· en· W2074668801 on OpenAlex
Farah Zahir, Jan M. Friedman

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueClinical Genetics · 2007
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomic variations and chromosomal abnormalities
Canadian institutionsBC Children's HospitalChildren's & Women's Health Centre of British ColumbiaUniversity of British Columbia
FundersGenome British ColumbiaWellcome TrustGenome Canada
KeywordsComparative genomic hybridizationGenomeCopy-number variationComputational biologyBiologyGeneticsClinical diagnosisMedicineGenePediatrics

Abstract

fetched live from OpenAlex

Our understanding of the causes of mental retardation is benefiting greatly from whole-genome scans to detect submicroscopic pathogenic copy number variants (CNVs) that are undetectable by conventional cytogenetic analysis. The current method of choice for performing whole-genome scans for CNVs is array genomic hybridization (AGH). Several platforms are available for AGH, each with its own strengths and limitations. This review discusses considerations that are relevant to the clinical use of whole-genome AGH platforms for the diagnosis of pathogenic CNVs in children with mental retardation. Whole-genome AGH studies are a maturing technology, but their high diagnostic utility assures their increasing use in clinical genetics.

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.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.665

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.328
GPT teacher head0.514
Teacher spread0.186 · 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