Clinical islet isolation and transplantation outcomes with deceased cardiac death donors are similar to neurological determination of death donors
Bibliographic record
Abstract
In islet transplantation, deceased cardiac death (DCD) donation has been identified as a potential extended source. There are currently no studies comparing outcomes between these categories, and our goal was to compare islet isolation success rates and transplantation outcomes between DCD and neurological determination of death (NDD) donors. Islet isolations from 15 DCD and 418 NDD were performed in our centre between September 2008 and September 2014. Donor variables, islet yields, metabolic function of isolated isled and insulin requirements at 1-month post-transplant were compared. Compared to NDD, pancreata from DCD were more often procured locally and donors required less vasopressive support (P < 0.001 and P = 0.023, respectively), but the other variables were similar between groups. Pre- and postpurification islet yields were similar between NDD and DCD (576 vs. 608 × 10(3) islet equivalent, P = 0.628 and 386 vs. 379, P = 0.881, respectively). The metabolic function was similar between NDD and DCD, as well as the mean decrease in insulin requirement at 1-month post-transplantation (NDD: 64.82%; DCD: 60.17% reduction, P = 0.517). These results support the broader use of DCD pancreata for islet isolation. A much larger DCD islet experience will be required to truly determine noninferiority of both short- and long-term outcomes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".