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Record W2593123211 · doi:10.1089/bio.2016.0077

Business Planning for a Campus-Wide Biobank

2017· article· en· W2593123211 on OpenAlexaffabout
Tamsin Tarling, Frances Lasser, Candace Carter, Lise Matzke, Gurm Dhugga, Nidhi Arora, Simon Dee, Jodi LeBlanc, Sindy Babinsky, Sheila O’Donoghue, Stefanie Cheah, Peter H. Watson, Suzanne Vercauteren

Bibliographic record

VenueBiopreservation and Biobanking · 2017
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsBC Cancer AgencyBC Children's HospitalRegent CollegeUniversity of British Columbia
Fundersnot available
KeywordsBiobankSustainabilityPlan (archaeology)BusinessCorporate governanceBusiness planMedicinePublic relationsPolitical scienceGeographyMarketingFinance

Abstract

fetched live from OpenAlex

Biobanks are resources that facilitate research. Many biobanks exist around the world, but most tend to focus on a specific disease or research area. BC Children's Hospital and BC Women's Hospital are located on the same campus (Oak Street Campus) in Vancouver, BC, Canada. A campus-wide biobank has been established on the site of these two hospitals to collect specimens and annotated data from children or women seeking medical care at either of the hospitals. Such an initiative requires careful planning and consideration of many factors such as buy in and support of key stakeholders, governance, financial planning, and optimizing specimen collection. We developed a business plan to account for the many aspects associated with integrating the "BC Children's Hospital BioBank." This document describes the approach our business plan took for the implementation of our biobank and the progress, including deviations from the business plan. We also provide a perspective on the current status with a focus on sustainability.

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)0.033
metaresearch head score (Gemma)0.024
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: Other · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0080.003
Scholarly communication0.0200.007
Open science0.0030.005
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0100.006

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 head0.591
GPT teacher head0.577
Teacher spread0.013 · 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
GenreOther

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

Citations9
Published2017
Admission routes2
Has abstractyes

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