Prognostic value of lymph node ratio in non-small-cell lung cancer: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: This meta-analysis aimed to investigate the prognostic value of lymph node ratio in non-small-cell lung cancer. METHODS: We searched systematically for eligible studies in PubMed, Web of Science, Medline (via Ovid) and Cochrane library through 6 November 2018. The primary outcome was overall survival. Disease-free survival and cancer-specific survival were considered as secondary outcomes. Hazard ratio with corresponding 95% confidence interval were pooled. Quality assessment of included studies was conducted. Subgroup analyses were performed based on N descriptors, types of tumor resection, types of lymphadenectomy and study areas. Sensitivity analysis and evaluation of publication bias were also performed. RESULTS: Altogether, 20 cohorts enrolling 76 929 patients were included. Mean Newcastle-Ottawa Scale was 7.65 ± 0.59, indicating the studies' quality was high. The overall result showed non-small-cell lung cancer patients with lower lymph node ratio was associated with better overall survival (HR: 1.946; 95% CI: 1.746-2.169; P < 0.001), disease-free survival (HR: 2.058; 95% CI: 1.717-2.467; P < 0.001) and cancer-specific survival (HR: 2.149; 95% CI: 1.864-2.477; P < 0.001). Subgroup analysis prompted types of lymphadenectomy and the station of positive lymph node have an important effect on the prognosis. No significant discovery was found in sensitivity analysis. CONCLUSION: Patients with lower lymph node ratio was associated with better survival, indicating that lymph node ratio may be a promising prognostic predictor in non-small-cell lung cancer. The type of lymphadenectomy, an adequate examined number and the removed stations should be considered for more accurate prognosis assessment.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.070 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".