Comparison of Clinical Characteristics and Outcomes in Relapsed Versus De Novo Metastatic Non–Small Cell Lung Cancer
Bibliographic record
Abstract
OBJECTIVES: To compare the clinical characteristics and outcomes between relapsed and de novo metastatic non-small cell lung cancer (NSCLC). MATERIALS AND METHODS: We reviewed all NSCLC diagnoses between January 1999 and December 2013 in the institutional Glans-Look Lung Cancer Database, which contains demographic, clinical, pathologic, treatment, and outcome information. Patients with distant metastasis at diagnosis (American Joint Committee on Cancer [AJCC] eighth edition, stage IV), the "de novo" cohort, were compared with the "relapsed" cohort, consisting of patients diagnosed with early stage disease (stage I/II) undergoing curative intent treatment and subsequently experiencing metastatic relapse. Survival analysis, along with univariate and multivariable analysis was performed. RESULTS: A total of 185 relapsed and 3039 de novo patients were identified. Significantly different patterns of smoking history, histology, systemic therapy use, and disease extent were observed between the relapsed and de novo cohorts. Median overall survival from time of metastasis was significantly longer in relapsed than in de novo disease (8.9 vs. 3.7 mo, P<0.001). Relapsed patients demonstrated significant improvements in outcomes over time. In multivariate analysis, de novo metastatic disease continued to bode a worse prognosis (adjusted hazard ratio [HR], 1.4) as did male sex (HR, 1.2), never-smoking history (HR, 1.2), and presence of extrapulmonary metastases (HR, 1.3). Systemic therapy receipt conferred better outcome (HR, 0.4), although the impact of relapsed versus de novo disease on outcomes persisted regardless of systemic therapy receipt. CONCLUSIONS: Relapsed and de novo patients represent significantly different subpopulations within metastatic NSCLC with the latter exhibiting poorer survival. This information facilitates discussions about prognosis with patients and supports screening initiatives aimed at reducing de novo disease.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".