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Record W3095488238 · doi:10.1089/bio.2020.0057

Transparency About Governance Contributes to Biobanks' Trustworthiness: Call for Action

2020· article· en· W3095488238 on OpenAlexaffabout
Felix Gille, Renata Axler, Alessandro Blasimme

Bibliographic record

VenueBiopreservation and Biobanking · 2020
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiobankTransparency (behavior)AccountabilityCorporate governanceBusinessPublic relationsPublic trustGood governanceAccountingPolitical scienceLawBiologyBioinformaticsFinance

Abstract

fetched live from OpenAlex

This article examines biobank transparency mechanisms vis-à-vis their public information, as found on the public biobank webpages. Two independent studies about biobank governance in Europe and Canada identified a lack of governance-related information provided by biobanks on their public webpages. This lack of transparency stands in contrast to governance best practice guidelines highlighting the importance of transparency as a principle of good governance. Transparency is especially important as many biobanks are publicly funded, and it contributes to accountability and supports the development of donor trust in biobanks. Empirical evidence shows that the public supports greater transparency about biobank governance. It will be important that information provided online is relevant and accessible for a variety of different stakeholders (e.g. public and private sector scientists and institutions, donors and potential donors, members of the public). Transparency standards, however, need to be proportionate to avoid the situation that only large-scale biobanks can allocate appropriate resources to fulfil them. Implementing adequate standards of transparency about biobanks' governance will increase accountability but also allow current and future participants to make more informed decisions about their participation in biobank activities.

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)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
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 head0.558
GPT teacher head0.531
Teacher spread0.027 · 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

Citations18
Published2020
Admission routes2
Has abstractyes

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