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Advances in nanomaterials for the diagnosis and treatment of head and neck cancers: A review

2022· review· en· W4294151573 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBioactive Materials · 2022
Typereview
Languageen
FieldMedicine
TopicPeptidase Inhibition and Analysis
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsHead and neckMedicineStage (stratigraphy)NanomaterialsIntensive care medicineNanotechnologySurgeryMaterials scienceBiology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.942
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.117
GPT teacher head0.411
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it