Enhancing efficiency and scientific impact of a clinical trials network: the Pediatric Heart Network Integrated CARdiac Data and Outcomes (iCARD) Collaborative
Bibliographic record
Abstract
Recent years have seen an exponential increase in the variety of healthcare data captured across numerous sources. However, mechanisms to leverage these data sources to support scientific investigation have remained limited. In 2013 the Pediatric Heart Network (PHN), funded by the National Heart, Lung, and Blood Institute, developed the Integrated CARdiac Data and Outcomes (iCARD) Collaborative with the goals of leveraging available data sources to aid in efficiently planning and conducting PHN studies; supporting integration of PHN data with other sources to foster novel research otherwise not possible; and mentoring young investigators in these areas. This review describes lessons learned through the development of iCARD, initial efforts and scientific output, challenges, and future directions. This information can aid in the use and optimisation of data integration methodologies across other research networks and organisations.
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 | 0.213 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".