Recurrence-Free Survival in Patients With Surgically Resected Non-Small Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Standard treatment for early-stage or locoregionally advanced non-small cell lung cancer (NSCLC) includes surgical resection. Recurrence after surgery is commonly reported, but a summary estimate for postsurgical recurrence-free survival (RFS) in patients with NSCLC is lacking. RESEARCH QUESTION: What is the RFS after surgery in patients with stage I-III NSCLC at different time points, and what factors are associated with RFS? STUDY DESIGN AND METHODS: A systematic search was performed in MEDLINE, EMBASE, and Cochrane databases between January 2011 and June 2021. The primary outcome was RFS at 1, 2, 3, and 5 years postresection. Single-arm, random-effects meta-analyses were done to calculate effect estimates and 95% CIs. Analyses were stratified by stage/substage as per the AJCC Cancer Staging Manual, and RFS was estimated (1) after pooling studies, using seventh or eighth edition staging criteria; and (2) among studies using only the eighth edition. Meta-regressions were performed to assess associations between RFS and patient demographic/clinical characteristics of interest. RESULTS: Data from 471 studies comprising 1,060 surgical study arms were extracted. RFS estimates from 60,695 patients staged with the seventh or eighth edition were analyzed. RFS ranged from 96% at 1 year postresection to 82% at 5 years for stage I, and from 68% at 1 year to 34% at 5 years for stage III. Estimates for patients staged using only eighth edition criteria were slightly higher. Older age, higher percentage of male patients, advancing stage, larger tumor size, and geographic region (North America/Europe vs Asia) were significantly associated with worse RFS. INTERPRETATION: This study presents a comprehensive assessment of reported RFS from published clinical literature, offering estimates at multiple postsurgical time points and by geographic region. Findings can inform treatment decisions, clinical trial design, and future research to improve outcomes among patients with 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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.017 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".