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Record W2739298907 · doi:10.1016/j.tig.2017.06.008

Clinical Genomics in Inflammatory Bowel Disease

2017· review· en· W2739298907 on OpenAlexafffund
Holm H. Uhlig, Aleixo M. Muise

Bibliographic record

VenueTrends in Genetics · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsBiologyInflammatory bowel diseaseGenomicsDiseaseInflammatory Bowel DiseasesComputational biologyGenomeGeneticsBioinformaticsInternal medicineGeneMedicine

Abstract

fetched live from OpenAlex

Genomic technologies inform the complex genetic basis of polygenic inflammatory bowel disease (IBD) as well as Mendelian disease-associated IBD. Aiming to diagnose patients that present with extreme phenotypes due to monogenic forms of IBD, genomics has progressed from 'orphan disease' research towards an integrated standard of clinical care. Advances in diagnostic clinical genomics are increasingly complemented by pathway-specific therapies that aim to correct the consequences of genetic defects. This highlights the exceptional potential for personalized precision medicine. IBD is nevertheless a challenging example for genomic medicine because the overall fraction of patients with Mendelian defects is low, the number of potential candidate genes is high, and interventional evidence is still emerging. We discuss requirements and prospects of explanatory and predictive clinical genomics in IBD.

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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.075
GPT teacher head0.402
Teacher spread0.327 · 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

Citations146
Published2017
Admission routes2
Has abstractno

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