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Record W1982772747 · doi:10.1155/2013/629681

Nanomedicine in Action: An Overview of Cancer Nanomedicine on the Market and in Clinical Trials

2013· article· en· W1982772747 on OpenAlexaff
Ruibing Wang, Paul S. Billone, Wayne M. Mullett

Bibliographic record

VenueJournal of Nanomaterials · 2013
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsNordion (Canada)
Fundersnot available
KeywordsNanomedicineMaterials scienceCancerAction (physics)NanotechnologyMedicineNanoparticleInternal medicine

Abstract

fetched live from OpenAlex

Nanomedicine, defined as the application of nanotechnology in the medical field, has the potential to significantly change the course of diagnostics and treatment of life‐threatening diseases, such as cancer. In comparison with traditional cancer diagnostics and therapy, cancer nanomedicine provides sensitive cancer detection and/or enhances treatment efficacy with significantly minimized adverse effects associated with standard therapeutics. Cancer nanomedicine has been increasingly applied in areas including nanodrug delivery systems, nanopharmaceuticals, and nanoanalytical contrast reagents in laboratory and animal model research. In recent years, the successful introduction of several novel nanomedicine products into clinical trials and even onto the commercial market has shown successful outcomes of fundamental research into clinics. This paper is intended to examine several nanomedicines for cancer therapeutics and/or diagnostics‐related applications, to analyze the trend of nanomedicine development, future opportunities, and challenges of this fast‐growing area.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0050.006
Open science0.0020.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0150.005

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 head0.203
GPT teacher head0.443
Teacher spread0.240 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations233
Published2013
Admission routes1
Has abstractyes

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