A Multicenter Study: North American Islet Donor Score in Donor Pancreas Selection for Human Islet Isolation for Transplantation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it