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Record W4200051455 · doi:10.3390/pharmaceutics14010041

Nanomedicine in Hepatocellular Carcinoma: A New Frontier in Targeted Cancer Treatment

2021· review· en· W4200051455 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

VenuePharmaceutics · 2021
Typereview
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreToronto General HospitalUniversity Health Network
FundersMitacsCanadian Liver Foundation
KeywordsHepatocellular carcinomaNanomedicineMedicineLiver cancerOncologyCancerInternal medicineCancer researchNanotechnology

Abstract

fetched live from OpenAlex

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death and is associated with a dismal median survival of 2-9 months. The fundamental limitations and ineffectiveness of current HCC treatments have led to the development of a vast range of nanotechnologies with the goal of improving the safety and efficacy of treatment for HCC. Although remarkable success has been achieved in nanomedicine research, there are unique considerations such as molecular heterogeneity and concomitant liver dysfunction that complicate the translation of nanotheranostics in HCC. This review highlights the progress, challenges, and targeting opportunities in HCC nanomedicine based on the growing literature in recent years.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
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 head0.107
GPT teacher head0.376
Teacher spread0.268 · 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