THE STANDARDIZATION OF PANCREATIC DONORS FOR ISLET ISOLATIONS
Bibliographic record
Abstract
P433 Aims: To standardize pancreatic donors for islet isolations. Introduction: Islet transplantation has proved to be a successful treatment for insulin dependent diabetes mellitus (IDDM). Donor variables such as age, cause of death, cold ischemia time (CIT), body mass index (BMI), and vasopressor levels have shown to be influential on the outcome of islet isolations. In addition to donor variables, the pancreas’ physical characteristics such as size, fat content, organ consistency, and the quality of the procurement can also be crucial to the outcome. In this study our aim was to establish an algorithm in which the combination of the donor quality and pancreas quality was given a numerical score from 0 to 100. Methods: In this study we retrospectively analyzed every pancreas that has been accepted since the inception of the clinical trial in 1999. In total 365 pancreata and the outcomes of their respective isolations were analyzed. Donor variables that were considered were donor age, CIT, BMI, cause of death, hospital stay, serum amylase or lipase levels, vasopressor levels, blood glucose, procuring team, social history and medical history. Within these categories ranges were established and points were assigned depending on the donors specific variable. For example, 20 points were allocated for donor age, the ranges and corresponding points awarded are as follows; < 25 years old = 5 points, 25 –34 years old = 10 points, 35 – 55 years old = 20 points, 56 – 65 years old = 10 points, > 65 years old = 5 points. Pancreata physical properties considered were quality of flush, pancreas consistency, quality of procurement, surgical damage, quality of packaging, and fat content. The staff of the clinical islet laboratory determined these factors at the time of cannulation; if the specific property was undesirable then points were deducted from the overall score. Results: Pancreata with a rating of 49.5 or less had a 0% transplant frequency (n = 0) and had a donor frequency of 7.67% (n = 28). Pancreata with a rating of 50 - 59.5 had a transplant frequency of 23.53% (n = 12) and a donor frequency of 13.97% (n = 51). Pancreata with a rating of 60 – 69.5 had a transplant frequency of 37.93% (n = 33) and a donor frequency of 23.84% (n = 87). Pancreata with a rating of 70 – 79.5 had a transplant frequency of 30.63% and a donor frequency of 30.41% (n = 111). Pancreata with a rating of 80 – 89.5 had a transplant frequency of 50.00% and a donor frequency of 19.72% (n = 72). Pancreata with a rating of 90 – 100 had a transplant frequency of 93.75% (n = 15) and accounted for 4.38% (n = 16) of all donors. We have seen an increase in the past five years for pancreata that rated less than 49.5. In 2003, we have also seen an 8% decrease compared to 2002 in donors with ratings of 80 – 89.5. Since the implementation of the rating system 5 of 7 isolations have been transplanted, 2 pancreata were not processed and 2 isolations were unsuccessful in terms of final islet yield. During this time there have also been 4 pancreata that were declined which would have otherwise been accepted under prior acceptability criteria. Conclusion: This scoring system has proven to be effective in assessing whether to accept or decline pancreata for processing. The rating system also allows for better management of an islet isolation processing facility as the cost of islet isolations are high and the algorithm removes all subjectivity. By analyzing the final score of the pancreas a more standardized decision can be made. Another benefit to the scoring system is that it allows for a quick and efficient way to trend the quality of donor organs.
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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 | 0.041 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".