[Early kidney-pancreas transplantation in type 1 diabetics prevents hemodialysis].
Bibliographic record
Abstract
BACKGROUND: The life-expectancy of type 1 diabetics (T1D) on dialysis is still shorter than that of non-diabetics. Pancreas transplantation (PT) in its different modalities should be considered as a life-saving procedure. METHODS: We analyzed our referral activity of T1D to PT from 1992. Since 2002, we have created a kidney and diabetes out-patient clinic devoted to the prevention of diabetic nephropathy and to the early referral of suitable T1D to combined kidney- pancreas transplantation (KPT) and isolated pancreas (PTA). RESULTS: In the last 14 yrs, 25 T1D underwent KP in our district (620000 inhabitants). At the beginning, KPT was performed abroad, but then the borders were closed. After stopping in the mid 1990s, KP activity restarted addressing preemptive KPT and PTA. Currently, only one patient is on dialysis while awaiting KPT. Four T1D were evaluated and excluded from the list on medical grounds; two patients are on the list and a further two patients are currently under evaluation. CONCLUSIONS: The implementation of a cooperative network among dialysis and transplant centers, supported by devoted out-patient clinics allowed the effective prevention of the dialysis requirement in T1D. Out-patient clinics devoted to diabetic nephropathy should play a pro-active role in preemptive KP, including the 'new' option of islet transplantation according to the Edmonton protocol.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".