Molecular-targeted therapies in the treatment of squamous cell carcinomas of the head and neck
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
PURPOSE OF REVIEW: The present study reviews recent developments of molecular-targeted therapies in the treatment of recurrent and/or metastatic head and neck squamous cell carcinoma. It also highlights ongoing research regarding predictive markers of sensitivity or resistance to anti-epidermal growth factor receptor agents and discusses some promising novel targets in head and neck squamous cell carcinoma, as well as clinical trial design challenges. RECENT FINDINGS: Phase III randomized studies have brought the proof that cetuximab, an anti-epidermal growth factor receptor agent, is able to improve survival, either in combination with radiation therapy or in first-line treatment for recurrent and/or metastatic head and neck squamous cell carcinoma. In addition, promising results have been obtained with antiangiogenic therapies in phase II trials. Some clinical and molecular markers of resistance to anti-epidermal growth factor receptor agents have been identified, but they have not yet been validated for clinical practice. Other interesting targets, such as insulin-like growth factor 1R or the PI3K/AKT/mTOR pathway, have been shown in vitro to play key roles in head and neck squamous cell carcinoma, and their inhibition warrants further evaluations. SUMMARY: Proof of the concept that molecular-targeted therapy is a valid therapeutic approach for head and neck squamous cell carcinoma has emerged with anti-epidermal growth factor receptor agents. Nevertheless, identification of predictive biomarkers of resistance or sensitivity to these therapies remains the main challenge in the optimal selection of patients most likely to benefit from them.
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) | 0.002 | 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