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Record W3121703135 · doi:10.1111/joim.13254

Nanotechnology for modern medicine: next step towards clinical translation

2021· review· en· W3121703135 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Internal Medicine · 2021
Typereview
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Cancer Society Research InstituteCanadian Institutes of Health Research
KeywordsMedicineTranslation (biology)Nanotechnology

Abstract

fetched live from OpenAlex

The field of nanotechnology has been a significant research focus in the last thirty years. This emphasis is due to the unique optical, electrical, magnetic, chemical and biological properties of materials approximately ten thousand times smaller than the diameter of a hair strand. Researchers have developed methods to synthesize and characterize large libraries of nanomaterials and have demonstrated their preclinical utility. We have entered a new phase of nanomedicine development, where the focus is to translate these technologies to benefit patients. This review article provides an overview of nanomedicine's unique properties, the current state of the field, and discusses the challenge of clinical translation. Finally, we discuss the need to build and strengthen partnerships between engineers and clinicians to create a feedback loop between the bench and bedside. This partnership will guide fundamental studies on the nanoparticle-biological interactions, address clinical challenges and change the development and evaluation of new drug delivery systems, sensors, imaging agents and therapeutic systems.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.142
GPT teacher head0.425
Teacher spread0.283 · 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