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Record W4407228793 · doi:10.1111/iej.14208

Environmental stimuli‐responsive hydrogels in endodontics: Advances and perspectives

2025· review· en· W4407228793 on OpenAlexaff
He Liu, Ya Shen

Bibliographic record

VenueInternational Endodontic Journal · 2025
Typereview
Languageen
FieldDentistry
TopicEndodontics and Root Canal Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSelf-healing hydrogelsEndodonticsNanotechnologyDentistryComputer scienceMaterials scienceMedicine

Abstract

fetched live from OpenAlex

Stimuli-responsive hydrogels are smart and functional materials that respond to various environmental stimuli, including temperature, light, magnetic field, pH, redox, enzymes and glucose. This responsiveness allows for the controlled release of therapeutic agents encapsulated within the hydrogels, enhancing treatment precision, improving therapeutic outcomes and minimizing side effects. Such hydrogels show great potential in root canal disinfection, management of dental pulp inflammation and pulp regeneration, making them promising candidates for more personalized and effective endodontic treatments. This article provides an overview of the latest advancements in the design and application of stimuli-responsive hydrogels in endodontics, emphasizing their potential to revolutionize endodontic treatments. It also addresses current challenges and explores future directions in the field, aiming to inspire and motivate researchers to further engage in or intensify their efforts within this promising area of research.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.349
Teacher spread0.318 · 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.

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

Citations12
Published2025
Admission routes1
Has abstractyes

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