A geographic information system dashboard for heart transplantation
Bibliographic record
Abstract
On March 22, 2023, the U.S. Health Resources and Services Administration (HRSA) announced plans to modernize the Organ Procurement and Transplantation Network (OPTN) to strengthen accountability, equity, and performance in the transplant system. 1 A highlight of the press release was the need for data dashboards detailing organ retrieval, waitlist outcomes, transplants, and demographic information.Similarly, in Canada, the Trillium Gift of Life Network (TGLN), Ontario Health, has also been interested in developing new methods to monitor access, allocation, and outcomes in heart transplantation.Numerous studies have shown that social determinants of health affect transplant equity and outcomes.2,3 An important component of social determinants of health is geography.Where patients live often reflect their socioeconomic status, proximity to health care access, center-dependent variations in medical practice, and local healthF I G U R E 1 Application architecture of the cardiac transplantation dashboard.Data from electronic medical records (EMR), patient demographics, and administrative health databases are integrated into a geo-database stored on secure cloud-based servers.Analytic results are then delivered to web and mobile apps using RESTful APIs.
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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.034 | 0.025 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".