Short- and mid-term outcomes of transanal versus laparoscopic total mesorectal excision for low rectal cancer: a meta-analysis
Bibliographic record
Abstract
PURPOSE: laparoscopic total mesorectal excision (LaTME) for low rectal cancer. METHODS: A systematic literature search was conducted using the web-based databases; China National Knowledge Infrastructure, Chinese BioMedical Database, PubMed, Embase, Cochrane Central Register of Controlled Trials, and Wanfang Database. Randomized controlled trials (RCTs) were evaluated using the Jadad scale and non-RCTs (NRCs) were evaluated using the Newcastle-Ottawa Scale. RESULTS: Ten studies (2 RCTs and 8 NRCs) involving 772 patients were included. Among them, 378 patients underwent TaTME and 394 patients underwent LaTME. Compared with the LaTME group, the conversion rate was low (risk ratio [RR], 0.25; 95% confidence interval [CI], 0.11-0.54; P < 0.001), the circumferential resection margin (CRM) involvement was low (RR, 0.48; 95% CI, 0.27-0.86; P = 0.010), and the hospital stay was short (mean difference, -1.72; 95% CI, -2.89 to -0.55; P = 0.004) in the TaTME group. No significant differences were seen in the mesorectal resection quality, CRM distance, distal resection margin (DRM) involvement, DRM distance, local R1 resection, intraoperative complications, morbidity, anastomotic leakage, severe morbidity, mortality, operative time, intraoperative blood loss, harvested lymph nodes, and local recurrence rate (P > 0.05). CONCLUSION: The TaTME is a promising surgical technique and is fully a safe and efficacious option in managing low rectal cancer.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.051 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 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".