Bibliographic record
Abstract
IntroductionSeveral large health data networks such as FDA Sentinel, PCORnet, and the Canadian Network of Observational Drug Effect Studies (CNODES) facilitate multi-site research using real-world electronic health data such administrative claims data, electronic health record data and registries. Experience in operation of mutliple health data networks will described. Objectives and ApproachOver the past 15 years substantial progress has been made in developing the optimal network operational design, governance, and technical architecture to facilitate the creation and operation of large-scale distributed health data networks. The design, architecture, and operation of a sustainable health data network requires balancing the needs of the network stakeholders such as funders, data sources, investigators, and regulatory bodies while enabling rapid and efficient use of data to support evidence generation and decision making. Important topics include protection of patient privacy, security, data autonomy, distributed analytics, data quality, and protection of confidential information. ResultsThe design and architecture of existing distributed health data networks provides guidance regarding the potential operational model for new networks and identifies areas of research to improve network functionality and capabilities. Most health data network adopt a common data model approach to facilitate multi-site querying and data quality assessment. This approach is coupled with distributed querying in which data partners maintain physical and operational control of their data. This design maximizes protection of confidential and proprietary information and minimizes the need to share patient-level data. Privacy-preserving distributed regression approaches and methods that obviate the need to share person-level data while generating robust results help to ensure network participation. Strong security and governance structures are also necessary for effective operation of a distributed network. Conclusion/ImplicationsDistributed health data networks offer the opportunity to use real-world data for public health surveillance and comparative safety and effectiveness research across large populations. The operational design, technical and analytic architecture, and governance models of networks drive their acceptance and success.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".