Video-Assisted Thoracic Surgery for Lung Cancer Resection
Bibliographic record
Abstract
Objective The purpose of this consensus conference was to determine whether video-assisted thoracic surgery (VATS) improves clinical and resource outcomes compared with conventional thoracotomy (OPEN) in adults undergoing lobectomy for lung cancer, and to outline evidence-based recommendations for the use of VATS in performing lobectomy for lung cancer. Methods Before the consensus conference, the best available evidence was reviewed in that systematic reviews, randomized trials, and nonrandomized trials were considered in descending order of validity and importance. At the consensus conference, evidence-based statements were created, and consensus processes were used to determine the ensuing recommendations. The American Heart Association/American College of Cardiology system was used to label the level of evidence and class of recommendation. Results and Recommendations The consensus panel agreed upon the following statements and recommendations in patients with clinical stage I nonsmall cell lung cancer undergoing lung lobectomy: 1. VATS can be recommended to reduce overall postoperative complications (class IIa, level A evidence). 2. VATS can be recommended to reduce pain and overall functionality over the short term (class IIa, level B evidence). 3. VATS can be recommended to improve delivery of adjuvant chemotherapy delivery (class IIa, level B evidence). 4. VATS can be recommended for lobectomy in clinical stage I and II non-small cell lung cancer patients, with no proven difference in stage-specific 5-year survival compared with open thoracotomy (class IIb, level B evidence).
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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 | 0.056 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".