[Meta-analysis of extralevator abdominoperineal excision for rectal cancer].
Bibliographic record
Abstract
OBJECTIVE: To evaluate the efficacy of extralevator abdominoperineal excision (ELAPE) of rectal cancer. METHODS: PubMed, Cochrane Library and Embase database were searched for clinical studies comparing the ELAPE and abdominoperineal excision (APE) for rectal cancer between 2007 and 2016. Two reviewers independently screened the articles and extracted the data. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the observational studies and the score more than 5 points was the inclusion criteria. Cochrane Handbook for Systematic Reviews of Interventions v5.1.0 was used to evaluate the quality of the randomized controlled trials (RCT). Intra-operative perforation rate, circumferential resection margin (CRM) involvement, local recurrence rate, perineal wound complications were brought into meta-analysis by Review Manager 5.3 software. RESULTS: A total of 556 articles were retrieved and 12 articles were enrolled finally, including 11 observational studies and 1 RCT study. All the 12 articles were high quality (scores of all observational studies were more than 11 points, RCT study accorded with 6 criteria of the quality evaluation). A total of 3 788 patients were enrolled, including 2 141 cases of ELAPE and 1 647 cases of APE. Meta-analysis revealed that intra-operative perforation rate of ELAPE was lower than APE (RR=0.52, 95%CI:0.34-0.79, P=0.002). There were no significant differences between two groups in CRM involvement (RR=0.72, 95%CI:0.49-1.07, P=0.10), local recurrence rate (OR=0.55, 95%CI:0.24-1.29, P=0.17) and perineal wound complications (RR=0.94, 95%CI:0.58-1.53, P=0.800). CONCLUSIONS: Compared with APE, ELAPE reduces the intra-operative perforation rate, and does not increase the perineal wound complications, but it has no advantages in decreasing the CRM involvement and local recurrence rate.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.053 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".