Comparison of postoperative inflammatory response between natural orifice specimen extraction surgery and conventional laparoscopy in the treatment of colorectal cancer: a meta-analysis and systematic review
Bibliographic record
Abstract
PURPOSE: Natural orifice specimen extraction surgery (NOSES) has attracted attention because of its minimal invasiveness. This meta-analysis compared inflammatory response profiles and infectious complications between colorectal cancer patients treated with NOSES and those treated with conventional laparoscopy. METHODS: Seven medical databases were searched up to February 2024. The authors included studies that examined changes in the inflammatory response and outcomes in the patients after NOSES surgery. The Cochrane tool and the Newcastle-Ottawa Scale were used to evaluate the quality of the studies. Pooled standardized mean differences and odds ratios with 95% CIs were calculated using either fixed- or random-effects models. Review Manager 5.4 (RevMan 5.4) and the R project were used for the meta-analysis. RESULTS: This meta-analysis included 22 studies. Pooled analyses revealed lower tumor necrosis factor-α levels (SMD=-1.34,95% CI [-2.43, -0.25]; Z=2.40, P =0.02 and SMD =-1.49,95% CI [-2.15, -0.82]; Z=4.36, P <0.0001) and C reactive protein levels (SMD=-0.56, 95% CI [-4.17, -2.50]; Z=2.19, P =0.03 and SMD =-1.24,95% CI [-1.77, -0.71]; Z=4.56, P <0.00001) on postoperative day 1 and postoperative day 3 for NOSES than for conventional laparoscopy. Pooled analysis revealed significantly lower interleukin-6 levels in the NOSES group (SMD=-1.88,95% CI [-2.84, -0.93]; Z=3.88, P =0.0001) on postoperative day 3. There were no significant differences in white blood cell count, procalcitonin levels, or the incidence of infectious complications between the two groups. CONCLUSIONS: NOSES has a superior inflammatory profile and does not increase the incidence of postoperative infectious diseases. The reported results should be validated in a larger population of colorectal cancer patients.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.042 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".