Global priorities for large-scale biomarker-based prospective cohorts
Bibliographic record
Abstract
The focus of this paper is on strategic approaches for establishing population-based prospective cohorts that collect and store biological samples from very large numbers of participants to help identify the determinants of common health outcomes. In particular, it aims to address key issues related to investigation of genetic, as well as social, environmental, and ancestral, diversity; generation of detailed genetic and other types of assay data; collection of detailed lifestyle and environmental exposure information; follow-up and characterization of incident health outcomes; and overcoming obstacles to data sharing and access (including capacity building). It concludes that there is a need for strategic planning at an international level (rather than the current ad hoc approach) toward the development of a carefully selected set of deeply characterized large-scale prospective cohorts that are readily accessible by researchers around the world.
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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 | 0.222 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".