A phase II trial of erlotinib in patients with recurrent malignant gliomas and nonprogressive glioblastoma multiforme postradiation therapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it