Variation in Availability and Ability to Share Data in a Global Pediatric Emergency Medicine Research Network
Bibliographic record
Abstract
OBJECTIVES: Electronic health record data holds promise for collaborative research involving very large sample sizes with diverse populations. We performed this study to determine, in an international network, the types of data available and the ease of obtaining such data, and to develop a qualitative understanding of privacy and data security regulatory frameworks. METHODS: We performed an electronic survey of members of the Pediatric Emergency Research Networks, a voluntary association of 8 research networks. The survey included (1) Likert scale responses for ease of obtaining specific data types; and (2) Likert scale and open-ended questions about barriers and enablers to sharing data internationally, including establishing ongoing clinical data registries. RESULTS: Of 263 surveyed, 127 (48%) responded. While ~25% of all sites can access data easily, more than 25% of sites reported moderate difficulty. Visit identifiers, patient identifiers (allowing tracking of patients longitudinally), and some emergency department (ED) visit data (eg, patient age, reason for visit, ED disposition, and ED length-of-stay) are generally easily obtained. Less easily available data include vital signs, clinical scores, medications, and laboratory and radiology results, which would require manual chart review at many sites. Some data are not collected at all in a substantial proportion of hospitals, including patient race, ethnicity, and preferred language. The regulatory framework around patient privacy and data security represented significant barriers to sharing data for some sites, including requiring informed consent to share data. CONCLUSIONS: Many research hospitals face significant barriers to sharing electronic health record data for research purposes.
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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 | 0.031 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".