The Comparison of Laparoscopic Colorectal Resection with Natural Orifice Specimen Extraction versus Mini-Laparotomy Specimen Extraction for Colorectal Tumours: A Systematic Review and Meta-Analysis of Short-Term Outcomes
Bibliographic record
Abstract
AIM: The aims of this study were to compare the short-term outcomes of natural orifice specimen extraction surgery (NOSES) and conventional laparoscopic surgery (CLAPS) for colorectal tumours and to evaluate the safety and feasibility of NOSES in colorectal resection. METHODS: A literature review was performed on the PubMed, Cochrane Library, and Embase databases up to March 2019. Papers conforming to the inclusion criteria were used for further analysis. The short-term outcomes included intraoperative outcomes and postoperative recovery results. The weighted mean difference (WMD) was calculated for continuous outcomes and odds ratio (OR) for dichotomous results. Study quality was evaluated using the Newcastle-Ottawa Quality Assessment Scale (NOS) or the 6-item Jadad scale. RESULTS: Eight studies comprising 686 patients met the inclusion criteria. Compared with CLAPS, NOSES had more advantages in terms of postoperative complications, postoperative pain, recovery of gastrointestinal function, duration of hospital stay, and cosmetic results. The lymph nodes harvested and intraoperative blood loss in NOSES were comparable with CLAPS; however, a prolonged operative time was observed in NOSES. CONCLUSIONS: NOSES was shown to be a safe and viable alternative to CLAPS in colorectal oncology in terms of short-term results. Further long-term and randomized trials are required.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.025 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".