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Record W1983885899 · doi:10.1159/000069544

Genebanks: A Comparison of Eight Proposed International Genetic Databases

2003· article· en· W1983885899 on OpenAlexaboutno aff
Melissa A. Austin, Sarah E Harding, Courtney McElroy

Bibliographic record

VenuePublic Health Genomics · 2003
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsnot available
FundersNational Institute of Environmental Health Sciences
KeywordsBiobankConfidentialityGovernment (linguistics)PopulationMEDLINEDatabaseMedicineBusinessEnvironmental healthPolitical scienceGeneticsComputer scienceBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: To identify and compare population-based genetic databases, or "genebanks", that have been proposed in eight international locations between 1998 and 2002. A genebank can be defined as a stored collection of genetic samples in the form of blood or tissue, that can be linked with medical and genealogical or lifestyle information from a specific population, gathered using a process of generalized consent. METHODS: Genebanks were identified by searching Medline and internet search engines with key words such as "genetic database" and "biobank" and by reviewing literature on previously identified databases such as the deCode project. Collection of genebank characteristics was by an electronic and literature search, augmented by correspondence with informed individuals. The proposed genebanks are located in Iceland, the United Kingdom, Estonia, Latvia, Sweden, Singapore, the Kingdom of Tonga, and Quebec, Canada. Comparisons of the genebanks were based on the following criteria: genebank location and description of purpose, role of government, commercial involvement, consent and confidentiality procedures, opposition to the genebank, and current progress. RESULTS: All of the groups proposing the genebanks plan to search for susceptibility genes for complex diseases while attempting to improve public health and medical care in the region and, in some cases, stimulating the local economy through expansion of the biotechnology sector. While all of the identified plans share these purposes, they differ in many aspects, including funding, subject participation, and organization. The balance of government and commercial involvement in the development of each project varies. Genetic samples and health information will be collected from participants and coded in all of the genebanks, but consent procedures range from presumed consent of the entire eligible population to recruitment of volunteers with informed consent. Issues regarding confidentiality and consent have resulted in opposition to some of the more publicized projects. None of the proposed databases are currently operational and at least one project was terminated due to opposition. CONCLUSIONS: Ambitious genebank projects have been proposed in numerous countries and provinces. The characteristics of the projects vary, but all intend to map genes for common diseases and hope to improve the health of the populations involved. The impact of these projects on understanding genetic susceptibility to disease will be increasingly apparent if the projects become operational. The ethical, legal, and social implications of the projects should be carefully considered during their development.

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)0.050
metaresearch head score (Gemma)0.111
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: none
Teacher disagreement score0.050
Threshold uncertainty score0.264

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.111
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0300.043
Science and technology studies0.0010.001
Scholarly communication0.0060.007
Open science0.0020.003
Research integrity0.0010.001
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 head0.066
GPT teacher head0.358
Teacher spread0.292 · 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

Citations107
Published2003
Admission routes1
Has abstractyes

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