Bibliographic record
Abstract
The application of minimally invasive approaches to pancreatic resection for benign and malignant diseases has been growing in the last two decades. Studies have demonstrated that laparoscopic distal pancreatectomy (LDP) is feasible and safe, and many of them show that compared to open distal pancreatectomy, LDP has decreased blood loss and length of hospital stay, and equivalent post-operative complication rates and short-term oncologic outcomes. LDP is becoming the procedure of choice for benign or small low-grade malignant lesions in the distal pancreas. Minimally invasive pancreaticoduodenectomy (MIPD) has not yet been widely adopted. There is no clear evidence in favor of MIPD over open pancreaticoduodenectomy in operative time, blood loss, length of stay or rate of complications. Robotic surgery has recently been applied to pancreatectomy, and many of the advantages of laparoscopy over open surgery have been observed in robotic surgery. Laparoscopic enucleation is considered safe for patients with small, benign or low-grade malignant lesions of the pancreas that is amenable to parenchyma-preserving procedure. As surgeons' experience with advanced laparoscopic and robotic skills has been growing around the world, new innovations and breakthrough in minimally invasive pancreatic procedures will evolve.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".