Impact of baseline characteristics on extensive‐stage SCLC patients treated with etoposide/carboplatin: A secondary analysis of a phase III study
Bibliographic record
Abstract
BACKGROUND: The purpose of the current study is to investigate the impact of baseline characteristics on the outcomes of extensive-stage small cell lung cancer (SCLC) patients recruited into a clinical trial. METHODS: This is a secondary analysis of the control arm (etoposide/carboplatin arm) of the 'NCT00363415' study which is a phase III study conducted between 2006 and 2007. Univariate analysis of factors affecting overall and progression-free survival (PFS) was conducted through Cox regression analysis [including age, race, gender, Eastern Cooperative Oncology Group performance score, body mass index, Lactate dehydrogenase, number of metastatic sites and brain metastases]. Factors with P < .05 in the univariate analysis were then included in the multivariate analysis. RESULTS: All patients within the control arm (etoposide/carboplatin) were included in the analysis (N = 455 patients). The following factors were predictive of worse overall survival (OS) in univariate analysis (P < .05): performance score = 2, LDH > upper limit of normal and ≥3 metastatic sites. Multivariate Cox regression analysis incorporating these three factors showed that only number of metastatic sites predicts worse OS (P < .0001). Likewise, the following factors were associated with worse PFS in univariate analysis (P < .05): performance score = 2 and ≥ 3 metastatic sites predict worse PFS (P < .05). Multivariate analysis incorporating these two factors showed that only number of metastatic sites predicts worse PFS (P < .0001). CONCLUSION: Number of metastatic sites is the most important predictive factor for overall and PFS among patients with extensive-stage SCLC treated with systemic chemotherapy within a clinical trial.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".