What are the costs, benefits, and harms of biomedical data reuse and of not reusing the data?
Bibliographic record
Abstract
This presentation reports on a scoping review of how the costs, benefits, harms, and cost savings associated with the reuse and non-reuse of biomedical data and samples are measured across health systems, research contexts, and populations. We report on how these outcomes have been quantified for different data types, stakeholder groups, populations, and areas of biomedical research using bibliometric, qualitative, and simulation approaches. We discuss gaps in the measurement of benefits, harms, costs and cost savings. We discuss how these measures and the impact of biomedical data reuse depends on the stakeholder group, context, data type, and governance conditions. We situate the discussion within broader questions of trust, transparency, and value creation in health data ecosystems, referencing case studies from Europe and Canada as well as insights from initiatives such as e-Estonia. Using a systems thinking lens, we argue that provenance metadata, living metrics, and stakeholder-specific value indicators are essential to achieving responsible, equitable data reuse.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.030 | 0.146 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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; both teacher heads agree on what is shown here.
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".