Does lateral lymph node dissection for low rectal cancer improve overall survival? Protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: radiochemotherapy - offers the best overall and recurrence-free survivals for these patients. METHODS AND ANALYSIS: We will perform a systematic review and meta-analysis aiming at determining the overall and recurrence-free survivals of patients with total mesorectum excision with and without lateral lymph node dissection, in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. MEDLINE, Embase, Cochrane and Web of Science will be searched from inception to the 16th of January 2019 for original studies written in English or in French including patients who benefited from lateral lymph node dissection for low rectal cancer and reporting overall survival for patients with and without lateral lymph node dissection. Hazard ratios of overall and recurrence-free survivals extracted from included studies will be combined and compared between patients with and without lateral lymph node dissection. Risk of bias will be assessed by using the Newcastle-Ottawa scale.The systematic review and meta-analysis protocol is registered in the International Prospective Register of Ongoing Systematic Reviews (PROSPERO) with number CRD42019123181. ETHICS AND DISSEMINATION: No ethical clearance is required for this study. This review will be published in a peer- reviewed journal and will be presented at various national and international conferences.
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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 | 0.052 | 0.082 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.022 | 0.029 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.005 |
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".