The Role of Staging Laparoscopy in Resectable and Borderline Resectable Pancreatic Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
AIM: The study aimed to determine the additional value of staging laparoscopy in patients with pancreatic cancer deemed potentially resectable based on computed tomography imaging. METHODS: A systematic literature search was performed using MEDLINE and the Cochrane Register of Controlled Trials (January 1995 to June 2017). Primary outcome measures were the overall yield and sensitivity to detect non-resectable disease. Quality of studies was assessed with the Newcastle-Ottawa Scale. RESULTS: From 156 records, 15 studies including 2,776 patients met the inclusion criteria. In 12 studies, reporting outcomes on 1,756 patients with resectable disease after standard imaging, 350 (20%, range 14-38%) cases of non-resectable cancer were detected with staging laparoscopy. In 3 studies on 242 patients with locally advanced disease after standard imaging, staging laparoscopy detected metastases in 86 patients (36%). The failure rate of staging laparoscopy to detect non-resectable disease was 5% (64 of 1,406). CONCLUSION: Staging laparoscopy reduces the non-therapeutic laparotomy rate, and in locally advanced or borderline resectable disease, staging laparoscopy could more accurately select patients for neoadjuvant protocols.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.023 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".