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Record W4405107121 · doi:10.3390/jcm13237449

Nanoparticle-Based Approaches in the Diagnosis and Treatment of Brain Tumors

2024· review· en· W4405107121 on OpenAlexaff
Parvin Pourmasoumi, Seyed Abdolvahab Banihashemian, Farshid Zamani, Aghdass Rasouli‐Nia, Davood Mehrabani, Feridoun Karimi‐Busheri

Bibliographic record

VenueJournal of Clinical Medicine · 2024
Typereview
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineBrain tumorPathology

Abstract

fetched live from OpenAlex

Glioblastomas are highly invasive brain tumors among perilous diseases. They are characterized by their fast proliferation and delayed detection that render them a significant focal point for medical research endeavors within the realm of cancer. Among glioblastomas, Glioblastoma multiforme (GBM) is the most aggressive and prevalent malignant brain tumor. For this, nanomaterials such as metallic and lipid nanoparticles and quantum dots have been acknowledged as efficient carriers. These nano-materials traverse the blood–brain barrier (BBB) and integrate and reach the necessary regions for neuro-oncology imaging and treatment purposes. This paper provides a thorough analysis on nanoparticles used in the diagnosis and treatment of brain tumors, especially for GBM.

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 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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.282
GPT teacher head0.444
Teacher spread0.162 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations10
Published2024
Admission routes1
Has abstractyes

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