Current indications for pancreas or islet transplant
Bibliographic record
Abstract
Pancreas or islet transplantation can provide good glycaemic control and insulin independence. Pancreas transplantation has been associated with improvement in diabetic retinopathy, nephropathy, neuropathy and vasculopathy, but has the associated morbidity of major surgery. Both forms of therapy require long-term immunosuppression and its attendant risks and both achieve insulin independence rates of about 80% at 1 year. Pancreas transplantation at the same time as a renal transplant is a worthwhile option to employ, especially if the diabetes has been difficult to control. Diabetes associated with frequent severe hypoglycaemia or extreme lability, despite optimization of diabetes management, may benefit from either pancreas or islet transplant alone with the latter being the lower-risk procedure. More quantitative measures of hypoglycaemia and lability are now available to facilitate the assessment of the severity of these problems with glucose control. Diabetic patients with renal involvement (macroproteinuria, but no major elevation of creatinine) and unstable diabetes may be helped with an islet or pancreas transplant, but this approach should still be considered experimental and such a transplant may hasten the need for renal replacement therapy. In the setting of well-controlled diabetes and intact renal function, it is difficult to justify pancreas or islet transplant alone given the risks of immunosuppression.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.014 |
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".