A Multicenter Study: North American Islet Donor Score in Donor Pancreas Selection for Human Islet Isolation for Transplantation
Bibliographic record
Abstract
Selection of an optimal donor pancreas is the first key task for successful islet isolation. We conducted a retrospective multicenter study in 11 centers in North America to develop an islet donor scoring system using donor variables. The data set consisting of 1,056 deceased donors was used for development of a scoring system to predict islet isolation success (defined as postpurification islet yield >400,000 islet equivalents). With the aid of univariate logistic regression analyses, we developed the North American Islet Donor Score (NAIDS) ranging from 0 to 100 points. The c index in the development cohort was 0.73 (95% confidence interval 0.70-0.76). The success rate increased proportionally as the NAIDS increased, from 6.8% success in the NAIDS < 50 points to 53.7% success in the NAIDS ≥ 80 points. We further validated the NAIDS using a separate set of data consisting of 179 islet isolations. A comparable outcome of the NAIDS was observed in the validation cohort. The NAIDS may be a useful tool for donor pancreas selection in clinical practice. Apart from its utility in clinical decision making, the NAIDS may also be used in a research setting as a standardized measurement of pancreas quality.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".