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Record W2247683003 · doi:10.1385/1-59259-187-6:03

CFTR Mutation Detection by Multiplex Heteroduplex (mHET) Analysis on MDE Gel

2003· article· en· W2247683003 on OpenAlexaff
Julian Zielenski, Isabel Aznarez, Tuncer Onay, John Tzounzouris, D. Markiewicz, Lap‐Chee Tsui

Bibliographic record

VenueHumana Press eBooks · 2003
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsUniversity of TorontoHospital for Sick Children
Fundersnot available
KeywordsHeteroduplexMutationGeneticsMendelian inheritanceMutation testingBiologyPopulationMultiplexGeneMedicine

Abstract

fetched live from OpenAlex

Mutation detection in an integral part of disease diagnosis and patient study. For most Mendelian diseases, multiple mutations may be found in a single gene among a patient population. The type of mutations may vary from large deletions to single-base-pair (bp) substitutions, and different diseases may have different predominant types. For example, large deletions are often found in Duchenne muscular dystrophy () and truncation mutation is the predominant type in BRCA1-associated breast cancer (). Therefore, different mutation detection strategies are required for different diseases.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.791

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 head0.041
GPT teacher head0.325
Teacher spread0.284 · 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

Citations28
Published2003
Admission routes1
Has abstractyes

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