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Record W1508622163

Biobanking of blood and bone marrow: emerging challenges for custodians of public resources.

2013· article· en· W1508622163 on OpenAlexaff
Lorena Aparicio, Wendy Lipworth, Shih‐Ning Then, Cameron Stewart, Patrick Coghlan, Ian Kerridge, Jennifer Fleming

Bibliographic record

VenuePubMed · 2013
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsBiobankCustodiansBusinessCompromiseValue (mathematics)TransplantationPublic relationsInstitutionBone marrow transplantationMedicineResource (disambiguation)Process (computing)Internet privacyPolitical scienceBioinformaticsLawComputer scienceBiologyGeography
DOInot available

Abstract

fetched live from OpenAlex

The Australian Bone Marrow Donor Registry (ABMDR) is a publicly funded company that is part of an international network that facilitates unrelated bone marrow transplantation. This role means that the ABMDR has access to a large biospecimen repository therefore making it a highly valuable research resource. Recognising the potential value of these biospecimens for research purposes, the ABMDR is in the process of determining whether, and how, to share its biospecimens with other biobanks. While this would undoubtedly be of value to the scientific community, and ultimately to the wider community, it would also inevitably transform the role of an institution whose primary role is therapeutic, and would compromise the degree of control that a custodian has over donated material. This article describe the challenges confronting the ABMDR, and organisations like it, in balancing their duties to donors, patients, researchers and the general public. These problems have led inevitably to the use of "property" rights language in the discussion of these issues but notions of gift, ownership, trusteeship and transfer might also be considered.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.987
Threshold uncertainty score0.187

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.053
GPT teacher head0.248
Teacher spread0.195 · 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 designOther design
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

Citations3
Published2013
Admission routes1
Has abstractyes

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