A phase II trial of erlotinib in patients with recurrent malignant gliomas and nonprogressive glioblastoma multiforme postradiation therapy
Bibliographic record
Abstract
Patients with (a) recurrent malignant glioma (MG): glioblastoma (GBM) or recurrent anaplastic glioma (AG), and (b) nonprogressive (NP) GBM following radiation therapy (RT) were eligible. Primary objective for recurrent MG was progression-free survival at 6 months (PFS-6) and overall survival at 12 months for NP GBM post-RT. Secondary objectives for recurrent MGs were response, survival, assessment of toxicity, and pharmacokinetics (PKs). Treatment with enzyme-inducing antiepileptic drugs was not allowed. Patients received 150 mg/day erlotinib. Patients requiring surgery were treated 7 days prior to tumor removal for PK analysis and effects of erlotinib on epidermal growth factor receptor (EGFR) and intracellular signaling pathways. Ninety-six patients were evaluable (53 recurrent MG and 43 NP GBM); 5 patients were not evaluable for response. PFS-6 in recurrent GBM was 3% with a median PFS of 2 months; PFS-6 in recurrent AG was 27% with a median PFS of 2 months. Twelve-month survival was 57% in NP GBMs post-RT. Primary toxicity was dermatologic. The tissue-to-plasma ratio normalized to nanograms per gram dry weight for erlotinib and OSI-420 ranged from 25% to 44% and 30% to 59%, respectively, for pretreated surgical patients. No effect on EGFR or intratumoral signaling was seen. Patients with NP GBM post-RT who developed rash in cycle 1 had improved survival (P < .001). Single-agent activity of erlotinib is minimal for recurrent MGs and marginally beneficial following RT for NP GBM patients. Development of rash in cycle 1 correlates with survival in patients with NP GBM after RT.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".