Comparison of single- and triple-port VATS for lung cancer: A meta-analysis
Bibliographic record
Abstract
Abstract Objective To compare the perioperative parameters between single- and triple-port video-assisted thoracoscopic surgery (VATS) lobectomy in the treatment of lung cancer. Methods The Pubmed, Embase, Cochrane library, and the Web of Science databases were electronically searched from inception to September 2019 for all relevant studies. Study quality was evaluated using the Jadad scale or the Newcastle-Ottawa scale. The results were pooled using the generic inverse-variance method and expressed as mean differences or risk ratios, with 95% confidence intervals. Results Three randomized controlled trials (RCTs) and ten cohort studies with 2,278 subjects were included in the meta-analysis. Whether based on RCTs or cohort studies, the pooled results showed no significant difference in the operation time, chest tube duration, intraoperative blood loss, postoperative hospital stays, lymph node dissection number, postoperative drainage volume, and postoperative complications between single- and triple-port VATS lobectomy (P > 0.05). Single-port VATS could relieve postoperative pain better than triple-port VATS, especially in the first day and fifth day (P < 0.05). No evidence of significant publication bias was found (P > 0.05). Conclusion Single-port VATS lobectomy can yield similar perioperative results to those of triple-port VATS lobectomy and is more effective in relieving postoperative pain.
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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) | 0.021 | 0.054 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".