Minimally Invasive Versus Conventional Colectomy: Evaluating Clinical Outcomes, Complications, and Recovery in Modern Surgical Practice
Bibliographic record
Abstract
Minimally invasive colectomy (MIC) has transformed colorectal surgery by improving recovery, reducing morbidity, and enhancing postoperative quality of life, yet variations in clinical outcomes, learning curves, and cost-effectiveness continue to challenge universal adoption. This systematic review synthesized current evidence comparing MIC and open colectomy (OC) across clinical, functional, and economic outcomes, following PRISMA 2020 guidelines. A structured search of PubMed, Embase, Scopus, Web of Science, and CENTRAL identified randomized controlled trials, multicenter cohorts, and meta-analyses published between 2015 and 2025. Studies were included only if they compared MIC and OC in adults (≥18 years) and reported extractable quantitative outcomes. Across eligible studies, MIC demonstrated a mean reduction in hospital length of stay (LOS) of approximately two to three days, an effect size ranging from 0.45 to 0.62 for postoperative morbidity reduction, and a 40-60% decrease in intraoperative blood loss, while maintaining comparable oncologic parameters to OC. Integration with Enhanced Recovery After Surgery (ERAS) protocols further improved bowel recovery, mobilization, and discharge timelines without increasing complications. Risk-of-bias assessments using Cochrane RoB 2.0 and the Newcastle-Ottawa Scale indicated predominantly low-risk evidence, strengthening confidence in the findings. Inclusion of elderly and emergency populations demonstrated that MIC remains safe and reproducible across complex settings. Although robotic colectomy increases operative time and cost, these drawbacks are offset by accelerated recovery and reduced readmissions. Collectively, the quantitatively reinforced evidence supports the growing role of MIC, particularly when combined with ERAS principles, as an efficient and patient-centered approach in modern colorectal practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".