Efficacy of total pancreatectomy with islet autotransplantation on opioid and insulin requirement in painful chronic pancreatitis: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: The rationale for total pancreatectomy in painful, treatment refractory, chronic pancreatitis is pain control. Concomitant islet cell autotransplantation can prevent the loss of islet cell function. This study aimed to systematically examine the impact of total pancreatectomy with islet cell autotransplantation on pain and quality of life. Methods: This meta-analysis was conducted according the Meta-analyses of Observational Studies in Epidemiology guideline. The Cochrane Library, PubMed, and Embase were searched for the following terms (1990 through April 2018): total pancreatectomy and chronic pancreatitis. Studies were included when addressing total pancreatectomy with islet cell autotransplantation for chronic pancreatitis in adults. Studies that reported no data on pain, endocrine function, or quality of life were excluded. Quality was assessed using the Newcastle-Ottawa scale for evaluation of all studies. Results: We included 15 observational studies evaluating 1,255 patients, of whom 28% had had endoscopic and 23% operative therapy. One year after total pancreatectomy with islet cell autotransplantation, the opioid-free rate had improved from between 0% and 15% to 63% (95% CI 46–77), and the insulin-free rate had decreased from between 89.5% and 100% to 30% (95% CI 20–43). An alcoholic etiology was associated with a lesser insulin-free rate after total pancreatectomy with islet cell autotransplantation. Quality of life improved statistically after total pancreatectomy with islet cell autotransplantation. Publication bias was present for both opioid and insulin outcomes. Conclusion: In selected patients with painful, treatment refractory, chronic pancreatitis, evidence shows that total pancreatectomy with islet cell autotransplantation is effective for pain control in almost two-thirds of patients, whereas the insulin-free rate is relatively low.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.044 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".