Diagnostic Laparoscopy with Ultrasound Still Has a Role in the Staging of Pancreatic Cancer: A Systematic Review of the Literature
Bibliographic record
Abstract
Background. The reported incidence of noncurative laparotomies for pancreatic cancer using standard imaging (SI) techniques for staging remains high. The objectives of this study are to determine the diagnostic accuracy of diagnostic laparoscopy with ultrasound (DLUS) in assessing resectability of pancreatic tumors. Study Design. We systematically searched the literature for prospective studies investigating the accuracy of DLUS in determining resectability of pancreatic tumors. Results. 104 studies were initially identified and 19 prospective studies (1,573 patients) were included. DLUS correctly predicted resectability in 79% compared to 55% for SI. DLUS prevented noncurative laparotomies in 33%. Of those, the most frequent DLUS findings precluding resection were liver metastases, vascular involvement, and peritoneal metastases. DLUS had a morbidity rate of 0.8% with no mortalities. DLUS remained superior to SI when analyzing studies published only in the last five years (100% versus 81%), enrolling patients after the year 2000 (74% versus 58%), or comparing DLUS to modern multidimensional CT (100% versus 78%). Conclusion. DLUS seems to still have a role in the preoperative staging of pancreatic cancer. With its ability to detect liver metastases, vascular involvement, and peritoneal metastases, the use of DLUS leads to less noncurative laparotomies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".