The Use of Systemic Treatment in the Maintenance of Patients with Non–Small Cell Lung Cancer: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Non-small cell lung cancer (NSCLC) is often diagnosed at later stages when treatment options are limited. Maintenance therapy may prolong the time to disease progression and potentially increase overall survival. Secondarily, it may increase the proportion of patients eligible for second-line therapy at the time of progression. The objective of this systematic review was to examine the use of systemic treatment in the maintenance of patients with NSCLC. METHODS: MEDLINE, EMBASE, and the Cochrane Library were searched for phase III randomized controlled trials comparing maintenance systemic treatment against another systemic treatment or placebo in patients with stage IIIB or IV NSCLC who had received a minimum of four prior cycles of platinum-based chemotherapy. Meta-analyses were conducted with clinically homogenous trials. RESULTS: Fourteen randomized controlled trials with 22 publications were included. The overall survival benefit was strongest for maintenance therapy with pemetrexed for patients with nonsquamous NSCLC (hazard ratio = 0.74, 95% confidence interval: 0.64-0.86) but not significant for patients with squamous NSCLC. There was also an overall survival benefit with maintenance therapy with epidermal growth factor receptor tyrosine kinase inhibitors, but the magnitude of the benefit was smaller than with pemetrexed (hazard ratio = 0.84, 95% confidence interval: 0.75-0.94). Docetaxel or gemcitabine as maintenance chemotherapies did not have an impact on overall survival. CONCLUSION: For patients with advanced, stable stage IIIB/IV NSCLC whose disease has not progressed after four to six cycles of platinum-based chemotherapy, the overall survival benefits were strongest for pemetrexed maintenance therapy followed by epidermal growth factor receptor tyrosine kinase inhibitor maintenance therapy.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".