Head and Neck Cancer Treatments through Chemotherapy to Magnetic Systems: Perspectives and Challenges
Bibliographic record
Abstract
BACKGROUND: Cancer is one of the diseases causing society's fears as a stigma of death and pain. Head and Neck Squamous Cell Carcinoma (HNSCC) is a group of malignant neoplasms of different locations in this region of the human body. It is one of the leading causes of morbidity and mortality in Brazil, because these malignant neoplasias, in most cases, are diagnosed in late phases. Surgical excision, chemotherapy and radiotherapy encompass the forefront of antineoplastic therapy; however, the numerous side effects associated with these therapeutic modalities are well known. Some treatments present enough potential to help or replace conventional treatments, such as Magnetic Hyperthermia and Photodynamic Therapy. Such approaches require the development of new materials at the nanoscale, able to carry out the loading of their active components while presenting characteristics of biocompatibility mandatory for biomedical applications. OBJECTIVE: This work aims to make a bibliographical review of HNSCC treatments. Recent techniques proven effective in other types of cancer were highlighted and raised discussion and reflections on current methods and possibilities of enhancing the treatment of HNSCC. METHODS: The study was based on bibliometric research between the years 2008 and 2019 using the following keywords: Cancer, Head and Neck Cancer, Chemotherapy, Radiotherapy, Photodynamic Therapy, and Hyperthermia. RESULTS: A total of 5.151.725 articles were found, 3.712.670 about cancer, 175.470 on Head and Neck Cancer, 398.736 on Radiotherapy, 760.497 on Chemotherapy, 53.830 on Hyperthermia, and 50.522 on Photodynamic Therapy. CONCLUSION: The analysis shows that there is still much room for expanding research, especially for alternative therapies since most of the studies still focus on conventional treatments and on the quest to overcome their side effects. The scientific community needs to keep looking for more effective therapies generating fewer side effects for the patient. Currently, the so-called alternative therapies are being used in combination with the conventional ones, but the association of these new therapies shows great potential, in other types of cancer, to improve the treatment efficacy.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".