Profile of erlotinib and its potential in the treatment of advanced ovarian carcinoma
Bibliographic record
Abstract
The epidermal growth-factor receptor (EGFR) is overexpressed in the majority of epithelial ovarian cancers and promotes cell proliferation, migration and invasion, and angiogenesis, as well as resistance to apoptosis. This makes EGFR an attractive therapeutic target in this disease. A number of strategies to block EGFR activity have been developed, including small-molecular-weight tyrosine kinase inhibitors such as erlotinib. Erlotinib has been evaluated as a single agent in recurrent ovarian cancer, as well as in combination with chemotherapeutic agents in the first-line and recurrent settings, and in combination with the antiangiogenic agent bevacizumab in the recurrent setting, as well as in the maintenance setting after completion of first-line chemotherapy. Unfortunately, erlotinib has shown only minimal efficacy as a single agent, and it has not enhanced the effects of chemotherapy or bevacizumab when combined with these agents. Ongoing and future studies of erlotinib and other agents blocking EGFR will need to define mechanisms resulting in resistance to such interventions, and to validate biomarkers of response to identify patients most likely to benefit from such approaches.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".