The Risk of Developing Periampullary Tumors After Cholecystectomy
Bibliographic record
Abstract
BACKGROUND: The link between cholecystectomy and the risk of periampullary tumors (PTs) is uncertain. The purpose of this systematic review and meta-analysis was to examine the available evidence on this association. METHODS: A systematic literature search was conducted in PubMed, Embase, and Web of Science for relevant studies published between 1996 and 2024. We considered studies that reported relative risks (RRs) for PTs after cholecystectomy. The Newcastle-Ottawa Scale was used to assess the risk of bias. RESULTS: The analysis includes 5 studies (n=107,476). A forest plot of individual study RRs and 95% CIs showed significant variation in the outcomes. A meta-analysis of 5 studies found a statistically significant increase in the hazard rate (HR) of primary tumors (PTs) after cholecystectomy, with a pooled HR of 1.48 (95% CI: 1.14-1.93; P <0.05). However, there was significant heterogeneity (I²=69%, P =0.01175), indicating an elevated risk of PTs associated with cholecystectomy. CONCLUSIONS: The existing evidence on the link between cholecystectomy and PTs risk is equivocal; however, a pooled study implies an elevated risk. More research, particularly large-scale prospective studies with established methodologies, is needed to better understand this link and inform therapeutic decision-making.
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 | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.018 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".