Robotic pancreaticoduodenectomy in patients with overweight or obesity: a meta-analysis protocol
Bibliographic record
Abstract
INTRODUCTION: The prevalence of overweight or obesity among patients undergoing pancreaticoduodenectomy is on the rise. The utilisation of robotic assistance has the potential to enhance the feasibility of performing minimally invasive pancreaticoduodenectomy in this particular group of patients who are at a higher risk. The objective of this meta-analysis is to assess the safety and effectiveness of robotic pancreaticoduodenectomy in individuals with overweight or obesity. METHODS AND ANALYSIS: This investigation will systematically search for randomised controlled trials (RCTs) and non-randomised comparative studies that compare robotic pancreaticoduodenectomy with open or laparoscopic pancreaticoduodenectomy in patients with overweight or obesity, using PubMed, Embase and the Cochrane Library databases. The methodological quality of studies will be evaluated using the Cochrane risk of bias tool for RCTs and the Newcastle-Ottawa Scale for observational studies. RevMan software (V.5.4.1) will be used for statistical analysis. The OR and weighted mean differences will be calculated separately for dichotomous and continuous data. The selection of a fixed-effects or random-effects model will depend on the level of heterogeneity observed among the included studies. ETHICS AND DISSEMINATION: This study will be conducted based on data in the published literature from publicly available databases. Therefore, ethics approval is not applicable. The results will be disseminated in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42023462321.
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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 | 0.041 | 0.054 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.004 |
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".