Minimally invasive left pancreatectomy for pancreatic ductal adenocarcinoma: review of the current literature
Bibliographic record
Abstract
The minimally invasive approach has gained popularity in the last decades, even in complex abdominal surgery such as pancreatic resections. Currently, many meta-analyses focus on the benefits and advantages of the minimally invasive approach compared to open surgery, especially during left pancreatectomy (LP). Limited data on the oncological outcomes are available. The review aims to describe the surgical and oncological outcomes of the minimally invasive left pancreatectomy (MILP). The search terms were based on the final histological pathology (pancreatic adenocarcinoma) and the comparison of different surgical approaches (open vs. minimally invasive). The search strategy was constructed in PubMed and adapted to run across other database platforms, focusing on studies published until 2022. A total of 2,878 studies were selected and duplicates were removed. After title and abstract screening, 109 articles remained for full-text assessment, of which 28 met the eligibility criteria for this systematic review. Considering the study design, the studies were divided into retrospective (n = 15), prospective (n = 4), and 13 propensity score-matched (n = 9). The present review of the literature suggests that MILP is technically feasible and safe for treating body and tail pancreatic ductal adenocarcinoma (PDAC). MILP did not have any impact on the major complications, reducing hospitalization. Regarding the oncological outcomes, the surgical technique did not have an impact on the R0 resection rate, lymph node harvested rate, use of adjuvant chemotherapy, and overall survival. Further prospective randomized trials remain indicated to assess the oncological impact of the MILP in patients with PDAC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".