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Record W2182749418 · doi:10.5206/uwomj.v82i2.4590

Nanotechnology in cancer therapeutics

2014· article· en· W2182749418 on OpenAlexvenueno aff
Denise Darmawikarta, Alexander Pazionis

Bibliographic record

VenueUniversity of Western Ontario Medical Journal · 2014
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsnot available
Fundersnot available
KeywordsNanotechnologyDrug deliveryCancer therapyCancerCancer treatmentNanomedicineCancer chemotherapyApplications of nanotechnologyMedicineNanoparticleMaterials scienceInternal medicine

Abstract

fetched live from OpenAlex

The field of cancer therapeutics is rapidly evolving. Of particular interest is the potential for nanotechnology to overcome one of chemotherapy’s biggest barriers: targeted drug delivery. Owing to the sheer small size of nanoparticles, the opportunity arises for chemotherapy to be administered much more accurately to cancer cells while sparing healthy adjacent tissues. In this article, we review the various tools in nanotechnology that have emerged as candidate delivery systems for chemotherapeutic agents. We discuss the ways in which nanotechnology has been demonstrated to eradicate cancer cells and comment on both successes and current limitations.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0080.004

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 head0.016
GPT teacher head0.231
Teacher spread0.215 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

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