Correlation between Development of Rash and Efficacy in Patients Treated with the Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor Erlotinib in Two Large Phase III Studies
Bibliographic record
Abstract
PURPOSE: Data from two large phase III studies were analyzed to characterize the correlation between the occurrence of rash during treatment with the epidermal growth factor receptor inhibitor erlotinib and improved clinical outcomes. EXPERIMENTAL DESIGN: Overall survival, progression-free survival (PFS), and tumor response were compared between patients in a rash-evaluable subset who did or did not develop rash in National Cancer Institute of Canada Clinical Trials Group Studies BR.21 (single agent in non-small-cell lung cancer, n = 444 in erlotinib group and n = 229 in placebo group) and PA.3 (combination with gemcitabine in pancreatic cancer, n = 254 in erlotinib plus gemcitabine group and n = 245 in placebo plus gemcitabine group). RESULTS: Presence of rash strongly correlated with overall survival in both studies. In Study BR.21, these correlations increased with rash severity grade: grade 1 versus no rash [hazard ratio (HR), 0.41, P < 0.001] and grade >or=2 versus no rash (HR, 0.29, P < 0.001). Similar results were observed for PFS. Disease control (complete response + partial response + stable disease) seemed to increase with the presence and severity of rash. In Study PA.3, grade >or=2 rash (but not grade 1) strongly correlated with overall survival improvement: grade >or=2 versus no rash (HR, 0.47, P < 0.001). Similarly, grade >or=2 rash was strongly correlated with improvements in PFS and disease control. CONCLUSIONS: Physicians and patients should view rash development as a positive event indicative of greater likelihood of clinical benefit. Further studies are required to identify patients most likely to develop rash and to determine if dose escalation to induce rash can improve efficacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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, 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".