Laparoscopic resection of pancreatic adenocarcinoma: Dream or reality?
Bibliographic record
Abstract
Laparoscopic pancreatic surgery is in its infancy despite initial procedures reported two decades ago. Both laparoscopic distal pancreatectomy (LDP) and laparoscopic pancreaticoduodenectomy (LPD) can be performed competently; however when minimally invasive surgical (MIS) approaches are implemented the indication is often benign or low-grade malignant pathologies. Nonetheless, LDP and LPD afford improved perioperative outcomes, similar to those observed when MIS is utilized for other purposes. This includes decreased blood loss, shorter length of hospital stay, reduced post-operative pain, and expedited time to functional recovery. What then is its role for resection of pancreatic adenocarcinoma? The biology of this aggressive cancer and the inherent challenge of pancreatic surgery have slowed MIS progress in this field. In general, the overall quality of evidence is low with a lack of randomized control trials, a preponderance of uncontrolled series, short follow-up intervals, and small sample sizes in the studies available. Available evidence compiles heterogeneous pathologic diagnoses and is limited by case-by-case follow-up, which makes extrapolation of results difficult. Nonetheless, short-term surrogate markers of oncologic success, such as margin status and lymph node harvest, are comparable to open procedures. Unfortunately disease recurrence and long-term survival data are lacking. In this review we explore the evidence available regarding laparoscopic resection of pancreatic adenocarcinoma, a promising approach for future widespread application.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".