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Record W1976475829 · doi:10.1002/humu.20631

A structured simple form for ordering genetic tests is needed to ensure coupling of clinical detail (phenotype) with DNA variants (genotype) to ensure utility in publication and databases

2007· article· en· W1976475829 on OpenAlexaff
Richard G.H. Cotton, Arleen D. Auerbach, A. F. Brown, Paola Carrera, John Christodoulou, Mireille Claustres, John G. Compton, D W Cox, Johan T. den Dunnen, Marc S. Greenblatt, Makoto Fujiwara, Pascale Hilbert, Atul N. Jani, Heikki Lehväslaiho, Daniel W. Nebert, Ishwar C. Verma, Mauno Vihinen

Bibliographic record

VenueHuman Mutation · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsMontreal General HospitalUniversity of Alberta
Fundersnot available
KeywordsGenetic dataBiologyGenotypeDatabaseComputer scienceData scienceGeneticsGeneMedicine

Abstract

fetched live from OpenAlex

Researchers and clinicians ideally need instant access to all the variation in their gene/locus of interest to efficiently conduct their research and genetic healthcare to the highest standards. Currently much key data resides in the laboratory books or patient records around the world, as there are many impediments to submitting this data. It would be ideal therefore if a semiautomated pathway was available, with a minimum of effort, to make the deidentified data publicly available for others to use. The Human Variome Project (HVP) meeting listed 96 recommendations to work toward this situation. This article is planned to initiate a strategy to enhance the collection of phenotype and genotype data from the clinician/diagnostic laboratory nexus. Thus, the aim is to develop universally applicable forms that people can use when investigating patients for each inherited disease, to assist in satisfying many of the recommendations of the HVP Meeting [Cotton et al., 2007]. We call for comment and collaboration in this article.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score0.431

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 head0.037
GPT teacher head0.344
Teacher spread0.307 · 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 designObservational
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

Citations11
Published2007
Admission routes1
Has abstractyes

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