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Record W4392108100 · doi:10.15173/m.v1i31.1846

Newborn Screening and Whole Genome Sequencing

2018· article· en· W4392108100 on OpenAlexvenueaboutno aff
Adam K. Wade‐Vallance

Bibliographic record

VenueThe Meducator · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsGenomeComputational biologyBiologyWhole genome sequencingGeneticsEvolutionary biologyMedicineGene

Abstract

fetched live from OpenAlex

Canadian newborn screening (NBS) programs aresuccessful in improving the prognosis of many affectedneonates with early-onset disorders by enabling rapiddiagnosis and treatment. However, many problems still existwith NBS programs including inconsistent coverage acrossprovinces or territories, gaps in disease screening, andvariable testing methodologies. Whole-genome sequencing(WGS) represents an increasingly cost-effective means ofscreening all genetic conditions with a single primary assay.Many benefits of WGS have been cited, including the ability to determine the exact causative mutation of a condition which could enable targeted therapy. Despite this, WGS in NBS carries serious ethical ramifications pertaining to disclosure of results, storage of data, and future development in NBS programs. Due to these factors, we are as-of-yet unprepared to incorporate WGS into Canadian NBS programs.

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 score0.946
Threshold uncertainty score0.205

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 head0.014
GPT teacher head0.248
Teacher spread0.234 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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