MétaCan
Menu
← Back to cohort
Record W4239919620 · doi:10.31219/osf.io/rvm3z

Genome-wide association studies (GWAS) have revolutionized our view of human health and disease genetics and offered novel gene therapy targets

2021· preprint· en· W4239919620 on OpenAlexaboutno aff
Moataz Dowaidar

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsnot available
Fundersnot available
KeywordsEthnic groupPopulationGenome-wide association studyGenomicsSociocultural evolutionDiseaseData sharingHuman genetic variation1000 Genomes ProjectHealth equityData scienceHuman genomeGeneticsBiologyHealth careMedicinePolitical scienceComputer scienceGenomeGenotypeSociologyGeneAlternative medicineDemography

Abstract

fetched live from OpenAlex

Knowing how genetic, behavioural, and sociocultural factors influence eachperson's risk for C. It is imperative that a larger, more diverse set of geneticstudies be done in order to be able to close the analysis of CMD distance Interms of disease prevention, there is a lot of interest in CMD genomic research.This potential can only be achieved if Ancestry DNA-style data like PRS issuccessfully gathered from the population. Inadequate participation is a bigissue in current CMD genetic research. Differential minorities in the UnitedStates and Canada have set forth some important steps to improve their accessto genetic research. To make sure that this will not happen again, these activitiesinclude discovering the issues and using community-based participatoryinterventions and benefits-sharing mechanisms. People underrepresented in theworld of genetics will require more services to support them.CMD and other genomics markers have been successfully identified and creatednovel avenues for human and population health change. In addition, it hascomplicated matters with regard to how this data would affect the broaderhealthcare system. What are the main questions: disproportionate difficulty inthe CMD genotype-phenotype database; confounded research on diseaseheritability; ethnicity may not be well described, making estimating diseaseheritability difficultDespite these roadblocks, genome-informed inclusive datawill bear unprecedented promise for bringing down CMD and improvingwellbeing. A large-scale data unification has already occurred, as mentioned inthis blog post to CMD Data sharing, however, is a project that must be done ona small scale in order to gain initial traction. This is also applicable to GWASresearch on self-identified ethnicity. Although race and ethnicity are sociallyand culturally constructed, the use of self-identifying categories in geneticstudies still endures.

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)0.013
metaresearch head score (Gemma)0.019
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: Methods · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.019
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.006
Science and technology studies0.0020.004
Scholarly communication0.0040.005
Open science0.0020.005
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0240.002

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 head0.044
GPT teacher head0.339
Teacher spread0.296 · 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
GenreMethods

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

Citations21
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicBRCA gene mutations in cancer→French-language works237,207→