The Differential Efficacy of Pemetrexed According to NSCLC Histology: A Review of Two Phase III Studies
Bibliographic record
Abstract
BACKGROUND: Recent studies of pemetrexed have identified a predictive role for non-small cell lung cancer (NSCLC) histology. We further reviewed the differential efficacy of pemetrexed according to histology in two large, phase III NSCLC trials. METHODS: One study tested pemetrexed versus docetaxel in previously treated patients (n = 571) and the other tested cisplatin plus pemetrexed versus cisplatin plus gemcitabine in chemotherapy-naive patients (n = 1,725) with advanced NSCLC. Cox proportional hazard models were used to test for covariate-adjusted treatment-by-histology interactions (THIs) for overall survival (OS) and progression-free survival (PFS). For each histologic subgroup, the Kaplan-Meier method was used to estimate unadjusted within-arm medians, and Cox models were used to estimate covariate-adjusted between-arm hazard ratios (HRs). RESULTS: In both studies, treatment arms were well balanced for histology. THIs were statistically significant (p < .005) for both OS and PFS. Nonsquamous patients treated with pemetrexed-based therapy experienced longer survival than the comparators (HR, 0.78 and 0.84, respectively), whereas squamous patients had shorter survival (HR, 1.56 and 1.23, respectively). Whereas the efficacy of pemetrexed regimens differed according to histology, it did not differ for docetaxel or for cisplatin plus gemcitabine. Pemetrexed was well tolerated across histologic groups. CONCLUSIONS: The consistency of these results across studies confirms the predictive effect of histology for pemetrexed and the survival advantage for pemetrexed in patients with nonsquamous histology. These analyses suggest pemetrexed should not be recommended for the treatment of squamous cell carcinoma, but, because of efficacy and safety advantages, pemetrexed may be preferable to other agents for treatment of patients with nonsquamous 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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".