How hard are the hard outcomes reported in national transplant registries?
Bibliographic record
Abstract
Obtaining information to determine patient death would seem to be an uncomplicated process. However, survival estimates can vary widely depending upon the data source and method of death ascertainment. For transplant candidates and recipients in the United States, deaths recorded in the Organ Procurement and Transplantation Network (OPTN) were dependent on transplant center reporting until 2004, when records from the Social Security Administration Death Master File (DMF) were incorporated to supplement transplant program reporting. In the OPTN, patients are assumed to be alive in the absence of a death record. Augmenting the Unites States transplant registry with external mortality data: A moving target ripe for further improvementAmerican Journal of TransplantationPreviewThe Organ Procurement and Transplantation Network conducts a robust death verification process when augmenting the United States transplant registry with external sources of data. Process enhancements added over 35,000 externally verified deaths across waitlist candidates and transplant recipients for all organs beginning in April 2022. Ninety-four percent of added posttransplant deaths occurred beyond 5 years posttransplant, and over 74% occurred beyond 10 years. Deceased donor solid organ recipients transplanted from January 1, 2010, through October 31, 2020, were analyzed from January and July 2022 Organ Procurement and Transplantation Network Standard Transplant Analysis and Research and the Scientific Registry of Transplant Recipients Standard Analysis Files to quantify the impact of including vs excluding unverified deaths (not releasable to researchers) on posttransplant patient survival estimates. Full-Text PDF
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 | 0.042 | 0.160 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.020 | 0.029 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".