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Record W2058351664 · doi:10.3109/10717541003762854

Polymeric drug delivery systems for localized cancer chemotherapy

2010· review· en· W2058351664 on OpenAlexaff
Raquel De Souza, Payam Zahedi, Christine Allen, Micheline Piquette‐Miller

Bibliographic record

VenueDrug Delivery · 2010
Typereview
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDrug deliveryDrugChemotherapyCancerCancer therapyCancer treatmentCancer chemotherapyMedicinePharmacologyIntensive care medicineNanotechnologyCancer researchMaterials scienceInternal medicine

Abstract

fetched live from OpenAlex

Cancer has become one of the most difficult health challenges of our time, accounting for millions of deaths yearly. Systemic chemotherapy is the most common therapeutic approach; however, considerable limitations exist including toxicities to healthy tissues and low achievable drug concentrations at tumor sites. More than 85% of human cancers are solid tumors, which can greatly benefit from localized delivery. This approach allows for high drug concentrations at the target site, lower systemic toxicity, and extended drug exposure which may be beneficial for cell cycle-specific drugs. Polymers have been widely considered in the development of localized delivery systems. This review focuses on both natural and synthetic biodegradable polymers that have been explored for localized chemotherapy, exploring their advantages, disadvantages, and clinical potential while citing examples of their use in pre-clinical development.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.003

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 head0.023
GPT teacher head0.292
Teacher spread0.269 · 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

Citations192
Published2010
Admission routes1
Has abstractyes

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