Efficacy vs. effectiveness: Erlotinib in previously treated non-small-cell lung cancer
Bibliographic record
Abstract
BACKGROUND: A randomized trial carried out by Shepherd et al. in patients with advanced or metastatic non-small-cell lung cancer showed statistically significant benefit of erlotinib over placebo in prolonging overall survival and progression-free survival. OBJECTIVES: The primary outcome was to compare overall survival of patients treated with erlotinib for non-small-cell lung cancer at Alberta Health Services - Cancer Care to the overall survival seen in the pivotal trial. Secondary outcomes included comparing progression-free survival, overall response rate, and duration of response between the two patient populations. METHODS: A retrospective review of charts was conducted for patients with locally advanced or metastatic non-small-cell lung cancer who received erlotinib therapy after failure of at least one prior chemotherapy regimen between 1 August 2006 and 31 July 2009. Survival data was analyzed using the Kaplan-Meier method. RESULTS: Median overall survival and progression-free survival were 5.19 months and 2.46 months, respectively, in Alberta Health Services - Cancer Care patients. The rate of response was 11% (median duration of response, 6.7 months). The likelihood of a response to erlotinib was higher among nonsmokers (p < 0.0001) and those with response to prior chemotherapy (p = 0.0896). In multivariate analysis, good performance status (p = 0.0109) and response to prior therapy (p < 0.0001) were favorable factors for survival. CONCLUSIONS: In a clinical setting, erlotinib does not perform as well in terms of median overall survival as reported in the pivotal trial (5.19 vs. 6.70 months).
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".