A Rapid Review on the Value of Biobanks Containing Genetic Information
Bibliographic record
Abstract
OBJECTIVES: Increasing access to health data through biobanks containing genetic information has the potential to expand the knowledge base and thereby improve screening, diagnosis, and treatment options for many diseases. Nevertheless, although privacy concerns and risks surrounding genetic data sharing are well documented, direct evidence in favor of the hypothesized benefits of data integration is scarce, which complicates decision making in this area. Therefore, the objective of this study is to summarize the available evidence on the research and clinical impacts of biobanks containing genetic information, so as to better understand how to quantify the value of expanding genomic data access. METHODS: Using a rapid review methodology, we performed a search of MEDLINE/PubMed and Embase databases; and websites of biobanks and genomic initiatives published from 2010 to 2022. We classified findings into 11 indicators including outputs (a direct product of the biobank activities) and outcomes (changes in scientific and clinical capacity). RESULTS: Of 8479 abstracts and 101 gray literature sources were reviewed, 96 records were included. Although most records did not report key indicators systematically, the available evidence concentrated on research indicators such as publications and gene-disorder association discoveries (63% of studies), followed by research infrastructure (26%), and clinical indicators (11%) such as supporting the diagnosis of individual patients. CONCLUSIONS: Existing evidence on the benefits of biobanks is skewed toward easily quantifiable research outputs. Measuring a comprehensive set of outputs and outcomes inspired by value frameworks is necessary to generate better evidence on the benefits of genomic data sharing.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.038 | 0.036 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".