Optimal Approach to Lobectomy for Non-Small Cell Lung Cancer: Systemic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: Video-assisted thoracic surgery (VATS) lobectomy was introduced over 25 years ago. More recently, the technique has been modified from a multiport video-assisted thoracic surgery (mVATS) to uniportal (uVATS) and robotic (rVATS), with proponents for each approach. Additionally most lobectomies are still performed using an open approach. We sought to provide evidence-based recommendations to help define the optimal surgical approach to lobectomy for early stage non-small cell lung cancer. METHODS: Systematic review and meta-analysis of articles searched without limits from January 2000 to January 2018 comparing open, mVATS, uVATS, and rVATS using sources Medline, Embase, and Cochrane Library were considered for inclusion. Articles were individually scrutinized by ISMICS consensus conference members, and evidence-based statements were created and consensus processes were used to determine the ensuing recommendations. The ACC/AHA Clinical Practice Guideline Recommendation Classification system was used to assess the overall quality of evidence and the strength of recommendations. RESULTS AND RECOMMENDATIONS: One hundred and forty-five studies met the predefined inclusion criteria and were included in the meta-analysis. Comparisons were analyzed between VATS and open, and between different VATS approaches looking at oncological outcomes (survival, recurrence, lymph node evaluation), safety (adverse events), function (pain, quality of life, pulmonary function), and cost-effectiveness. Fifteen statements addressing these areas achieved consensus. The highest level of evidence suggested that mVATS is preferable to open lobectomy with lower adverse events (36% versus 42%; 88,460 patients) and less pain (IIa recommendation). Our meta-analysis suggested that overall survival was better (IIb) with mVATS compared with open (71.5% versus 66.7% 5-years; 16,200 patients). Different VATS approaches were similar for most outcomes, although uVATS may be associated with less pain and analgesic requirements (IIb). CONCLUSIONS: This meta-analysis supports the role of VATS lobectomy for non-small cell lung cancer. Apart from potentially less pain and analgesic requirement with uVATS, different minimally invasive surgical approaches appear to have similar outcomes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".