Total Pancreatectomy With Islet Autotransplantation as an Alternative to High-risk Pancreatojejunostomy After Pancreaticoduodenectomy
Bibliographic record
Abstract
OBJECTIVE: To compare pancreaticoduodenectomy (PD) and total pancreatectomy (TP) with islet autotransplantation (IAT) in patients at high risk of postoperative pancreatic fistula (POPF). BACKGROUND: Criteria to predict the risk of POPF occurrence after PD are available. However, even when a high risk of POPF is predicted, TP is not currently accepted as an alternative to PD, because of its severe consequences on glycaemic control. Combining IAT with TP may mitigate such consequences. METHODS: Randomized, open-label, controlled, bicentric trial (NCT01346098). Candidates for PD at high-risk pancreatic anastomosis (ie, soft pancreas and duct diameter ≤3 mm) were randomly assigned (1:1) to undergo either PD or TP-IAT. The primary endpoint was the incidence of complications within 90 days after surgery. RESULTS: Between 2010 and 2019, 61 patients were assigned to PD (n=31) or TP-IAT (n=30). In the intention-to-treat analysis, morbidity rate was 90·3% after PD and 60% after TP-IAT ( P =0.008). According to complications' severity, PD was associated with an increased risk of grade ≥2 [odds ratio (OR)=7.64 (95% CI: 1.35-43.3), P =0.022], while the OR for grade ≥3 complications was 2.82 (95% CI: 0.86-9.24, P =0.086). After TP-IAT, the postoperative stay was shorter [median: 10.5 vs 16.0 days; P <0.001). No differences were observed in disease-free survival, site of recurrence, disease-specific survival, and overall survival. TP-IAT was associated with a higher risk of diabetes [hazard ratio=9.1 (95% CI: 3.76-21.9), P <0.0001], but most patients maintained good metabolic control and showed sustained C-peptide production over time. CONCLUSIONS: TP-IAT may become the standard treatment in candidates for PD, when a high risk of POPF is predicted.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 0.000 |
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".