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Record W1984771506 · doi:10.1002/ajmg.c.10016

Advances in molecular cytogenetics for the evaluation of mental retardation

2003· review· en· W1984771506 on OpenAlexaff
Jie Xu, Zhong Chen

Bibliographic record

VenueAmerican Journal of Medical Genetics Part C Seminars in Medical Genetics · 2003
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomic variations and chromosomal abnormalities
Canadian institutionsMcMaster UniversityMcMaster University Medical Centre
Fundersnot available
KeywordsMolecular cytogeneticsCytogeneticsMicrodissectionKaryotypeBiologyFluorescence in situ hybridizationComparative genomic hybridizationComputational biologyGeneticsChromosomeGenotypingGene

Abstract

fetched live from OpenAlex

Recent years have witnessed rapid advances in molecular cytogenetics and its impact in studying mental retardation (MR). We review new molecular cytogenetic methods, including interphase fluorescence in situ hyrbridization (FISH), comparative genomic hybridization (CGH), multicolor karyotyping, telomere FISH, primed in situ labeling (PRINS), genotyping, microdissection, and microarray for the evaluation of MR. These new methods are very useful in two major aspects: further characterization of chromosome abnormalities as detected with routine banding analysis, including additions, duplications, deletions, translocations, markers, or complex aberrations; and screening for "hidden" chromosome aberrations in patients with an apparently normal karyotype. These new methods have great diagnostic potential in prenatal, postnatal, and preimplantational settings. Although powerful, at this point, they are primarily research tools in nature. It is essential that these new methods be used in conjunction with standard methods in order to maximize obtainable information for better management of patients with MR.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.004
Science and technology studies0.0010.002
Scholarly communication0.0010.003
Open science0.0020.001
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0050.006

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 head0.031
GPT teacher head0.373
Teacher spread0.342 · 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

Citations81
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Medical Genetics Part C Seminars in Medical GeneticsSame topicGenomic variations and chromosomal abnormalitiesFrench-language works237,207