A Systematic Review and Meta-Analysis of Open vs. Laparoscopic Resection of Gastric Gastrointestinal Stromal Tumors
Bibliographic record
Abstract
Gastric gastrointestinal stromal tumors (GISTs) are the most common sarcoma of the gastrointestinal tract, and surgical resection is the primary treatment of early disease. Limited data exist concerning laparoscopic resections of these neoplasms. This systematic review was designed to evaluate the literature comparing laparoscopic and open surgical resection of gastric GISTs and to assess the effectiveness and safety of this minimally invasive technique. We performed a systematic search of MEDLINE, the Cochrane Library, PubMed, Embase, Scopus, Web of Science, Google Scholar, the clinical trials database and ProQuest Dissertations and Theses as well as the past 3 years of conference abstracts from the Society of American Gastrointestinal and Endoscopic Surgeons Annual Meetings. Studies comparing the open and the laparoscopic approaches to the resection of gastric GISTs were included in this systematic review. Two reviewers independently performed the screen of titles and abstracts, the full manuscript review, the data extraction and the risk of bias assessment. A quantitative analysis was performed. Of the 189 studies identified, seven studies were included. The laparoscopic approach was associated with a significantly lower length of hospital stay (3.82 days (2.14 - 5.49)). There was no observed difference in operative time, adverse events, estimated blood loss, overall survival and recurrence rates. This study supports that laparoscopic resection is safe and effective for gastric GISTs and is associated with a significantly lower length of hospital stay. Further trials are needed for cost analysis and to rigorously assess oncologic outcomes.
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 | 0.018 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.032 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".