Bibliographic record
Abstract
The first laparoscopic cholecystectomy was performed in the mid-1980s. Since then, laparoscopic surgery has continued to gain prominence in numerous fields, and has, in some fields, replaced open surgery as the preferred operative technique. The role of laparoscopy in staging cancer is controversial, with regards to gallbladder carcinoma, pancreatic carcinoma, hepatocellular carcinoma and liver metastasis from colorectal carcinoma, laparoscopy in conjunction with intraoperative ultrasound has prevented nontherapeutic operations, and facilitated therapeutic operations. Laparoscopic cholecystectomy is the preferred option in the management of gallbladder disease. Meta-analyses comparing laparoscopic to open distal pancreatectomy show that laparoscopic pancreatectomy is safe and efficacious in the management of benign and malignant disease, and have better patient outcomes. A pancreaticoduodenectomy is a more complex operation and the laparoscopic technique is not feasible for this operation at this time. Robotic assisted pancreaticoduodenectomy has been tried with limited success at this time, but with continuing advancement in this field, this operation would eventually be feasible. Liver resection remains to be the best management for hepatocellular carcinoma, cholangiocarcinoma and colorectal liver metastases. Systematic reviews and meta-analyses have shown that laparoscopic liver resections result in patients with equal or less blood loss and shorter hospital stays, as compared to open surgery. With improving equipment and technique, and the incorporation of robotic surgery, minimally invasive liver resection operative times will improve and be more efficacious. With the incorporation of robotic surgery into hepatobiliary surgery, donor hepatectomies have also been completed with success. The management of benign and malignant disease with minimally invasive hepatobiliary and pancreatic surgery is safe and efficacious.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.069 | 0.027 |
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".