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Record W2556546522 · doi:10.1089/bio.2016.0021

The Canadian Tissue Repository Network Biobank Certification and the College of American Pathologists Biorepository Accreditation Programs: Two Strategies for Knowledge Dissemination in Biobanking

2016· article· en· W2556546522 on OpenAlexaffabout
Rebecca Barnes, Katheryn Shea, Peter H. Watson

Bibliographic record

VenueBiopreservation and Biobanking · 2016
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsUniversity of British ColumbiaBC Cancer AgencyCanadian Respiratory Research Network
Fundersnot available
KeywordsBiobankBiorepositoryAccreditationCertificationMedicineMedical educationMEDLINEPolitical scienceBioinformaticsBiology

Abstract

fetched live from OpenAlex

As health research increasingly relies on biospecimens and associated data, new demands have emerged for biorepositories to provide assurances of the quality of their overall operations, not just assurances of the quality of the biospecimens and data that they hold. The biobanking community has responded in various ways, including the creation of two different programs to disseminate biobanking best practices. This article describes in detail the Canadian Tissue Repository Network (CTRNet) Biobank Certification Program and the College of American Pathologists (CAP) Biorepository Accreditation Program. Despite differences in their approaches, these programs share one key element-assessment of biobanking practices by an external organization. In the absence of a single internationally endorsed biobanking best practices dissemination program, the CTRNet and CAP programs provide two different solutions, each contributing to the pursuit of enhanced quality in biobanking.

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)0.004
metaresearch head score (Gemma)0.003
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 score0.545
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.178
GPT teacher head0.468
Teacher spread0.290 · 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

Citations28
Published2016
Admission routes2
Has abstractyes

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