Efficacy of single- and double-hole thoracoscopic lobectomy for treatment of non-small cell lung cancer: a meta-analysis.
Bibliographic record
Abstract
OBJECTIVES: To compare the effectiveness of single-port and double-port thoracoscopic lobectomy in the treatment of non-small cell lung cancer (NSCLC) using meta-analysis. METHODS: We systematically searched Pubmed, Embase, and Cochrane Library databases to collect literature on single-hole and double-hole thoracoscopic lobectomy for NSCLC with the end date of August 2022. Keywords included "thoracoscopy", "lobectomy", and "non-small cell lung cancer". Two authors independently conducted literature screening, data extraction, and quality evaluation. The quality evaluation tools were the Cochrane bias risk assessment tool and the Newcastle-Ottawa scale. Meta-analysis was performed using RevMan5.3 software. The odds ratio (OR), weighted mean difference (WMD), and 95% Cl were calculated using a fixed-effects model or random-effect model as appropriate. RESULTS: = 0.46] had no statistical significance. CONCLUSION: Single-hole thoracoscopic lobectomy has advantages in reducing intraoperative bleeding volume, alleviating early postoperative pain, and shortening postoperative hospital stay time. Double-hole thoracoscopic lobectomy has advantages in lymph node dissection. Both methods are equally safe and feasible for NSCLC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.053 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".