Moving EGFR Targeted Therapy into the Induction Phase of the Management of Squamous Cell Carcinoma of the Head and Neck
Bibliographic record
Abstract
Many advances in the treatment of squamous cell carcinoma of the head and neck have occurred in the past few years. Since the advent of cetuximab, a chimeric monoclonal antibody against epidermal growth factor receptor, the search for other efficacious targeted therapies has awakened the interest and curiosity of researchers and clinicians. Initially, cetuximab demonstrated effectiveness as single agent in heavily pretreated patients diagnosed with head and neck cancer, and has demonstrated to improve locoregional control and survival when combined with radiotherapy. Thesuccess of cetuximab has transitioned to other settings and with different modalities such as in combination with other conventional cytotoxic agents in the metastatic setting, combined with radiation therapy as part of concurrent treatment, and lately, in combination with other agents in the induction phase of the sequential approach. In this review, we discuss all different modalities in combination with cetuximab and how cetuximab has been incorporated into other clinical settings with only one goal in mind: improve the survival rates of our patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".