Advances in nanomaterials for the diagnosis and treatment of head and neck cancers: A review
Bibliographic record
Abstract
Nanomaterials (NMs) have increasingly been used for the diagnosis and treatment of head and neck cancers (HNCs) over the past decade. HNCs can easily infiltrate surrounding tissues and form distant metastases, meaning that most patients with HNC are diagnosed at an advanced stage and often have a poor prognosis. Since NMs can be used to deliver various agents, including imaging agents, drugs, genes, vaccines, radiosensitisers, and photosensitisers, they play a crucial role in the development of novel technologies for the diagnosis and treatment of HNCs. Indeed, NMs have been reported to enhance delivery efficiency and improve the prognosis of patients with HNC by allowing targeted delivery, controlled release, responses to stimuli, and the delivery of multiple agents. In this review, we consider recent advances in NMs that could be used to improve the diagnosis, treatment, and prognosis of patients with HNC and the potential for future research.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".