Association of a Palliative Surgical Approach to Stage IV Pancreatic Neuroendocrine Neoplasms with Survival: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The role of primary tumor resection in patients with pancreatic neuroendocrine neoplasms (PanNENs) and unresectable distant metastases remains controversial. We aimed to evaluate the effect of palliative primary tumor resection (PPTR) on overall survival (OS) in this setting. We searched the MEDLINE, Embase, Cochrane Library, Web of Science and SCOPUS databases up to January 2020 and used the Newcastle-Ottawa scale (NOS) criteria to assess quality/risk of bias. A total of 5661 articles were screened. In 10 studies, 5551 unique patients with stage IV PanNEN and unresectable metastases were included. The five-year OS for PanNEN patients undergoing PPTR in stage IV was 56.6% vs. 23.9% in the non-surgically treated patients (random effects relative risk (RR): 1.70; 95% CI: 1.53-1.89). Adjusted analysis of pooled hazard ratios (HR) confirmed longer OS in PanNEN patients undergoing PPTR (random effects HR: 2.67; 95% CI: 2.24-3.18). Cumulative OS analysis confirmed an attenuated survival benefit over time. The complication rate of PPTR was as high as 27%. In conclusion, PPTR may exert a survival benefit in stage IV PanNEN. However, the included studies were subject to selection bias, and special consideration should be given to PPTR anchored to a multimodal treatment strategy. Further longitudinal studies are warranted, with long-term follow-up addressing the survival outcomes associated with surgery in stage IV disease.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.005 | 0.006 |
| 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".