1093. Infectious Complications after Pancreatic Islet Transplantation
Bibliographic record
Abstract
Abstract Background Despite the significant advancement in islet transplantation over the past three decades, our understanding of infectious complications post islet transplant remains limited. Methods This is a single center retrospective review of Islet transplant recipients at the University of Alberta between February 2006 and December 2015. All infectious episodes events occurring after transplant were categorized as opportunistic and non-opportunistic. Results We analyzed 142 patients receiving a median of 2 islet transplants per patient, with 18 patients receiving 1 transplant (13%), 77 (54%) 2, 33 (23%) 3, 13 (9%) 4 and 1(1%) 5 transplants. Median age at first transplant was 50 years and 85 (47%) were male. Lymphocyte depleting agent with thymoglobulin or alemtuzumab was used for induction in 94% in first and 53% in second transplant. CMV serostatus was CMV D+/R- 61 (43%), CMVD+/R+ 52 (37%), CMVD-/R+ 16 (11%) and CMVD-/R- 13 (9%). CMV infection occurred in 21 patients (15%) [CMVD+/R- 6 (9.8%) and CMVR+ 15 (22.1%), p=0.06]. Other opportunistic infections included VZV 7 (4.9%), Nocardia 3(2.1%), and Pneumocystis jirovecii pneumonia 1. Non-opportunistic infections included skin and soft tissue infection 14 (9.9%), urinary tract infection 11 (7.7%), pneumonia 7 (4.9%) clostridium difficile infection (CDI) 4 (2.8%), and non-CDI gastroenteritis 5 (3.5%) (Table 1). Table 1: Infectious Complication post islet transplant Conclusion Although the rate of infections after islet cell transplant is less frequent than other types of transplants, opportunistic infections, especially CMV, are not uncommon and should be considered in this setting. Disclosures Carols Cervera, MD, PHD, Merk (Grant/Research Support, Scientific Research Study Investigator, Advisor or Review Panel member, Other Financial or Material Support, Lecture fees) James Shapiro, MD, PHD FRCS(Eng) FRCSC MSM FRSC, ViaCyte (Consultant) Dima Kabbani, MD, Merck (Research Grant or Support)
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".