Localized Drug Delivery Systems: An Update on Treatment Options for Head and Neck Squamous Cell Carcinomas
Bibliographic record
Abstract
Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers in the world, with surgery, radiotherapy, chemotherapy, and immunotherapy being the primary treatment modalities. The treatment for HNSCC has evolved over time, due to which the prognosis has improved drastically. Despite the varied treatment options, major challenges persist. HNSCC chemotherapeutic and immunotherapeutic drugs are usually administered systemically, which could affect the patient's quality of life due to the associated side effects. Moreover, the systemic administration of salivary stimulating agents for the treatment of radiation-induced xerostomia is associated with toxicities. Localized drug delivery systems (LDDS) are gaining importance, as they have the potential to provide non-invasive, patient-friendly alternatives to cancer therapy with reduced dose-limiting toxicities. LDDSs involve directly delivering a drug to the tissue or organ affected by the disease. Some of the common localized routes of administration include the transdermal and transmucosal drug delivery system (DDSs). This review will attempt to explore the different treatment options using LDDSs for the treatment of HNSCC and radiotherapy-induced damage and their potential to provide a better experience for patients, as well as the obstacles that need to be addressed to render them successful.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".