Sharing and Safeguarding Pediatric Data
Bibliographic record
Abstract
Data sharing is key to advancing our understanding of human health and well-being. While issues related to pediatric research warrant strong ethical protections, overly protectionist policies may serve to exclude minors from data sharing initiatives. Pediatric data sharing is critical to scientific research concerning health and well-being, to say nothing of understanding human development generally. For example, large-scale pediatric longitudinal studies, such as those in the DREAM-BIG Consortium, on the influence of prenatal adversity factors on child psychopathology, will provide prevention data and generate future health benefits. Recent initiatives have formulated sound policy to help enable and foster data sharing practices for pediatric research. To help translate these policy initiatives into practice, we discuss how model consent clauses for pediatric research can help address some of the issues and challenges of pediatric data sharing, while enabling data sharing.
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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 | 0.334 | 0.485 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.007 | 0.025 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier 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".