Comparison of postoperative complications in mediastinal lymph node dissection versus mediastinal lymph node sampling for early stage non-small cell lung cancer: Protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Lung cancer is the primary cause of cancer-related deaths worldwide, with high rates of morbidity and mortality. The most effective treatment for early stage (I-II) non-small cell lung cancer (NSCLC) is surgical resection. However, the extent of mediastinal lymph nodes removal required and the impact of their removal remains controversial. This systematic review and meta-analysis aimed to evaluate the postoperative complications in patients with stage I-II NSCLC who received mediastinal lymph node dissection (MLND) or mediastinal lymph node sampling (MLNS). METHODS AND ANALYSIS: According to the predefined inclusion criteria, we will conduct a comprehensive search for randomized controlled trials (RCTs) and observational studies examining the postoperative complications of MLND compared to MLNS in patients with stage I-II NSCLC. The search will be performed across multiple databases including PubMed, Embase, the Cochrane Library, CNKI, WanFang, Sinomed, VIP, Duxiu, and Web of Science from inception to February 2024. Additionally, relevant literature references will be retrieved and hand searching of pertinent journals will be conducted. Screening, data extraction, and quality assessment will be performed by two independent reviewers. Review Manager 5.4 will be applied in analyzing and synthesizing. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) will be used to assess the quality of evidence for the whole RCTs and used Newcastle-Ottawa scale to assess the methodologic quality of observational studies. ETHICS AND DISSEMINATION: This study did not include personal information. Ethical approval was not required for this study. This study is based on a secondary analysis of the literature, so ethical review approval is not required. The final report will be published in a peer-reviewed journal. CONCLUSION: This systematic review will contribute to compare the safety and survival benefits of these two surgical techniques for the treatment of early stage NSCLC, to further guide the selection of surgical approaches. TRIAL REGISTRATION: The protocol of the systematic review has been registered on Open Science Framework, with a registration number of DOI https://doi.org/10.17605/OSF.IO/N2Y5D.
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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 | 0.055 | 0.072 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.025 | 0.037 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.004 |
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".