Survival outcomes of Segmentectomy and Lobectomy for early stage non-small cell lung cancer: a Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background The question of whether segmentectomy and lobectomy have similar survival outcomes for patients with early stage non-small cell lung cancer (NSCLC) is a matter of debate. Methods A cohort study and randomized controlled trial were conducted, comparing segmentectomy and lobotomy, by utilizing computerized access to the Pubmed, Web of Science, and Cochrane Library databases up until July 2022. The Cochrane Collaboration tool was used to evaluate the randomized controlled trials, while the Newcastle-Ottawa Scale (NOS) was used to evaluate the cohort studies. Sensitivity analyses were also carried out. Results The analysis incorporated 18 literature studies, including one randomized controlled trial and 17 cohort studies, and was divided into a segmentectomy group (n = 2081) and a lobectomy group (n = 2395) based on the type of surgery the patient underwent. Each study was followed up from 27 months to 130.8 months after surgery. OS:HR = 1.14, 95%CI(0.97,1.32), P = 0.10; DFS:HR = 1.13, 95%CI(0.91,1.41), P = 0.27; RFS:HR = 0.95, 95%CI(0.81,1.12), P = 0.54. Conclusion The results of the study suggest that the survival outcomes of the segmentectomy group were not inferior to that of the lobectomy group. Segmentectomy should therefore be considered as a treatment option for early stage NSCLC.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".