PostgREST Data Provider for React-Admin: Bootstrap the creation of user interfaces on top of PostgreSQL databases
Bibliographic record
Abstract
In today’s data-driven world, vast amounts of data are stored in relational databases like i2b2, often using middleware applications for delivery. PostgreSQL, a widely used open-source DBMS, offers advanced features, including Foreign Data Wrappers (FDWs) for integration with other DBMSs. However, accessing data typically requires SQL knowledge. RESTful APIs simplify data interactions, and tools like PostgREST convert PostgreSQL databases into RESTful APIs. Our work introduces a PostgREST Data Provider that bridges React-Admin with PostgREST. A demo application showcases its capabilities, using KeyCloak for authentication and integrating an i2b2 database with FDW, fuzzy full-text search with ZomboDB, and utilizing GRASCCO discharge letters linked to i2b2 patients. • Seamlessly integrate PostgREST APIs with React-admin for efficient data management. • Demonstration app showcases advanced search with ZomboDB and a full-text search using GRASCCO and FHIR representation. • Leverage PostgreSQL foreign data wrappers (FDWs) to interact with diverse data sources seamlessly. • Unified authentication with KeyCloak supports multiple data providers in one application. • Open-source provider under MIT license encourages customization and enhancement.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.047 | 0.047 |
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".