Specific humoral rejection of a pancreas allograft in a recipient of pancreas after kidney transplantation
Bibliographic record
Abstract
A 46 year-old male patient who had received a pancreas transplant, subsequent to a kidney transplant (PAK) for diabetes nephropathy, was admitted to our transplant unit for an acute hyperglycaemia associated with a mild increase of creatinine. His type 1 diabetes was declared around his 5th birthday and was complicated by a proliferative retinopathy, nephropathy, arteritis and ischaemic cardiomyopathy. After a 4-year period of haemodialysis, a renal transplantation was performed in August 1996 from a deceased donor sharing only one human leukocyte antigen (HLA) DR Ag (Table 1). After a short period of delayed graft function, renal function improved and stabilized at a creatinine level of around 140 μmol/l. Initial immunosuppression included induction treatment with anti-T lymphocyte globulins (Thymoglobulin, Genzyme, USA) and a maintenance regimen associating tacrolimus and azathioprine. No episode of rejection occurred. Seven years later, in July 2003, the patient received an isolated pancreas transplant from a 45-year-old female deceased donor. At this time, he had classes I and II HLA panel reactive antibodies (PRA) of 0 and 3%, respectively. He had previously undergone three blood transfusions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".