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Record W4365791453 · doi:10.1017/pma.2023.2

Programmable polymers with shape memory for biomedical applications

2023· article· en· W4365791453 on OpenAlexafffund
Kaojin Wang, Limin Man, Meng Zhang, Yong‐Guang Jia, X. X. Zhu

Bibliographic record

VenueProgrammable Materials · 2023
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsUniversité de Montréal
FundersNatural Science Foundation of Shandong ProvinceFonds Québécois de la Recherche sur la Nature et les TechnologiesCanada Research Chairs
KeywordsShape-memory polymerComputer sciencePolymerNanotechnologyDrug deliveryMaterials scienceBiomedical engineeringEngineeringComposite material

Abstract

fetched live from OpenAlex

Abstract Shape memory polymers (SMPs) are a type of programmable materials capable of transforming their shapes in a pre-programmed way upon the application of an external stimulus. These materials have been tested for various potential applications particularly in the biomedical field for polymers with general and specific requirements. This review focuses on the recent advances in biomedical applications, including self-tightening sutures, pressure bandages, self-expansion stents, tissue engineering scaffolds, artificial muscles, drug delivery, and orthodontic archwires, after a brief description of the concepts, classifications, programming procedures, and material requirements of SMPs.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.018
GPT teacher head0.269
Teacher spread0.251 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations12
Published2023
Admission routes2
Has abstractyes

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