Systematic review and meta-analysis comparing outcomes of multi-port versus single-incision laparoscopic surgery (SILS) in Hartmann’s reversal
Bibliographic record
Abstract
BACKGROUND: Colostomy formation as part of the Hartmann's procedure is often performed during emergency surgery as a damage limitation measure where attempts at bowel anastomosis and continuity are contraindicated. Hartmann's reversal (HR) remains challenging and can be attempted through open surgery and various minimally invasive techniques (laparoscopic and robotic platforms). We aimed to analyse outcomes of conventional multi-port laparoscopy (CL) versus single-incision approach (SILS) in patients undergoing HR. METHODS: A comprehensive online search of various databases was conducted in accordance with PRISMA guidelines including Medline, PubMed, Embase, and Cochrane. Comparative studies of patients undergoing CL and SILS for HR were included. Analysed primary outcomes were total operative time and mortality rate. Secondary outcomes included post-operative complications, length of hospital stay, risk of visceral injury intra-operatively, and re-operation rate. Combined overall effect sizes were calculated using the random-effects model, and the Newcastle-Ottawa Scale (NOS) was used to assess bias. RESULTS: Two observational studies matching our inclusion criteria with a total of 160 patients (SILS 100 vs. CL 60) were included. Statistical difference was observed for one outcome measure: operative duration (MD - 44.79 CI - 65.54- - 24.04, P < 0.0001). No significant difference was seen in mortality rate (OR 1.66 CI 0.17-16.39, P = 0.66), overall post-operative complications (OR 0.60 CI 0.28-1.32, P = 0.20), length of stay (MD - 0.22 CI - 4.25-3.82, P = 0.92), Clavien-Dindo III + complications (OR 0.61 CI 0.15-2.53, P = 0.50), risk of visceral injury (OR 1.59 CI 0.30-8.31, P = 0.58), and re-operation rates (OR 0.73 CI 0.08-6.76, P = 0.78). CONCLUSION: Accounting for study limitations, the SILS procedure seems to be quicker with non-inferior outcomes compared with the conventional multi-port approach. This may lead to better patient satisfaction and cosmesis and potentially reduce the risk of future incisional hernia occurrence. However, well-designed, randomised studies are needed to draw more robust conclusions and recommendations.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".