Identifying patients with non-small cell lung cancer (NSCLC) unlikely to benefit from erlotinib: An exploratory analysis of National Cancer of Institute of Canada Clinical Trials Group BR.21
Bibliographic record
Abstract
7161 Background: Despite a 9% response rate, BR.21 demonstrated significant survival benefit for patients receiving erlotinib as 2nd/3rd line therapy for NSCLC. We undertook to characterize, by exploratory subset analysis, patients less likely to benefit from erlotinib. To identify factors for consideration, we first identified baseline characteristics associated with early progression by eight wks and early death by 3 mos. Methods: Using stratification factors and potential prognostic factors from BR.21, the Cox regression model with stepwise selection was used to establish a prognostic model to separate erlotinib patients into 4 risk categories based on the 10th, 50th & 90th percentiles of prognostic index scores. 7 variables (smoking history, PS, weight loss, anemia, high LDH, response to prior chemo and time from diagnosis to randomization) were used in the final model. The hypothesis was that the characteristics of the treated patients in the highest risk group would also be predictive of lack of benefit from erlotinib when erlotinib and placebo patients with the same characteristics were compared. Results: Factors associated with PD by 8 wks were: PS2–3 (p = 0.009), weight loss (p = 0.0004), anemia (p = 0.008), PD to prior chemo (p = 0.006), non-Asian (p = 0.047), EGFR IHC-negative (p = 0.005), Factors associated with survival < 3 mos were: PS2–3 (p < 0.0001), weight loss (p < 0.0001), anemia (p < 0.0001), PD to prior chemo (p < 0.0001), non-Asian (p = 0.008), high LDH (p < 0.0001), time to randomization <12 mos (p = 0.0003). Comparison of overall survival for the 4 risk groups derived from prognostic index score as follows: high benefit (HR = 0.41, p = 0.007), 2 intermediate benefit (HR 0.79, p = 0.09; HR 0.80; p = 0.09); no benefit (HR 1.23; p = 0.42). Median survivals for erlotinib (placebo) patients in each group were 17.3 (8.3), 9.7 (7.5), 4.1 (3.7), 1.9 (2.7) mos. Conclusions: By establishing a prognostic model, we identified a small group of patients who are unlikely to benefit from 2nd/3rd line erlotinib therapy. This model requires prospective validation to confirm that it is both prognostic and predictive of outcome from treatment. [Table: see text]
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".