Ghost Ileostomy Versus Loop Ileostomy Following Oncologic Resection for Rectal Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective The aim of this study was to compare ghost ileostomy (GI) and loop ileostomy (LI) in patients undergoing oncologic resection for rectal cancer in terms of postoperative morbidity. Summary Background Data LIs are often fashioned to protect downstream anastomoses following oncologic resection for low rectal cancer at medium-to-high risk of anastomotic leak. More recently, GIs have been utilized in patients with low-to-medium risk anastomoses to reduce the rate of unnecessary stomas. Methods Medline, Embase, and CENTRAL were systematically searched. Studies investigating the use of GI in patients undergoing oncologic resection for rectal cancer were included. The primary outcomes were anastomotic leak and postoperative morbidity. Secondary outcomes included stoma-related complications and length of stay (LOS). Pairwise meta-analyses were performed with inverse variance random effects. Results From 242 citations, 14 studies with 946 patients were included. In comparative studies, 359 patients were undergoing GI and 266 patients were undergoing LI. Pairwise meta-analysis revealed no differences in the prevalence of anastomotic leak (OR 1.40, 95%CI .73-2.68, P = .31), morbidity (OR .76, 95%CI .44-1.30, P = .32), or LOS (SMD -.05, 95%CI -.33-.23, P = .72). International Study Group of Rectal Cancer anastomotic leak grades were as follows: Grade A (GI 0% vs LI 13.3%), Grade B (GI 80.9% vs LI 86.7%), Grade C (GI 19.1% vs LI 0%). Conclusions GI appears to be a safe alternative to LI following oncologic resection for rectal cancer. Larger, prospective comparative studies are warranted to evaluate the use of GI in patients deemed to be at low-to-medium risk of anastomotic leak.
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".