Validation of a New North American Islet Donor Score for Donor Pancreas Selection and Successful Islet Isolation in a Medium-Volume Islet Transplant Center
Bibliographic record
Abstract
The selection of optimal pancreas donors is one of the key factors in determining the ultimate outcome of clinical islet isolation. North American Islet Donor Score (NAIDS) allows for estimating the chance of the success of islet isolation. It was developed based on the data from over 1000 donors from 11 islet isolation centers and validated in the University of Alberta, Edmonton, on the cohort from the most active islet transplant center. Now we aimed to also validate it in our much less active program. Areas under the receiver operating characteristic curves (AUROCs) and logistic regression analyses were obtained to test if NAIDS would better predict successful islet isolation (defined as post-purification islet yield >400,000 islet equivalents (IEQ)) than previously described Edmonton islet donor score (IDS) and our modified version of IDS. We analyzed the donor scores with reference to 82 of our islet isolation outcomes. The success rate increased proportionally as NAIDS increased, from 0% success in NAIDS < 50 points to 40% success in NAIDS ≥ 80 points. AUROCs were 0.67 (95% confidence interval (CI) 0.55-0.79) for NAIDS, 0.58 (95% CI 0.44-0.71) for modified IDS, and 0.51 (95% CI 0.37-0.65) for IDS and did not differ significantly. However, based on logistic regression analyses, NAIDS was the only statistically significant predictor of successful isolation (p = 0.01). The main advantage of NAIDS is an enhanced ability to discriminate poor-quality donors than previously used scoring systems at University of Chicago, with 0% chance for success when NAIDS was <50 as compared with 40% success rate for IDS <50. NAIDS was found to be the most useful available tool for donor pancreas selection in clinical and research practice in our center, allowing for identification and rejection of poor-quality donors, saving time and resources.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".