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Record W2106154206 · doi:10.1586/erd.12.24

Temperature-sensitive polymers for drug delivery

2012· review· en· W2106154206 on OpenAlexaff
Scott D. Fitzpatrick, Lindsay E. Fitzpatrick, Ajit Thakur, Mohammad A. Jafar Mazumder, Heather Sheardown

Bibliographic record

VenueExpert Review of Medical Devices · 2012
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsUniversity of TorontoMcMaster University
Fundersnot available
KeywordsDrug deliveryNanotechnologyComputer scienceTargeted drug deliveryDrugScaffoldRisk analysis (engineering)Biochemical engineeringMedicineMaterials sciencePharmacologyEngineering

Abstract

fetched live from OpenAlex

The ability to undergo rapid changes in response to subtle environmental cues make stimuli- responsive materials attractive candidates for minimally invasive, targeted and personalized drug delivery applications. This special report aims to highlight and provide a brief description of several of the significant natural and synthetic temperature-responsive materials that have clinical relevance for drug delivery applications. This report examines the advantages and disadvantages of natural versus synthetic materials and outlines various scaffold architectures that can be utilized with temperature-sensitive drug delivery materials. The authors provide a commentary on the current state of the field and provide their insight into future expectations for temperature-sensitive drug delivery, emphasizing the importance of the emergence of dual and multiresponsive systems capable of responding precisely to an expanding set of stimuli, thereby allowing the development of disease-specific drug delivery vehicles.

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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.007

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 head0.028
GPT teacher head0.338
Teacher spread0.310 · 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

Citations63
Published2012
Admission routes1
Has abstractyes

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