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Record W4390175776 · doi:10.3390/ma17010086

Hydrogels in Ophthalmology: Novel Strategies for Overcoming Therapeutic Challenges

2023· article· en· W4390175776 on OpenAlexaff
Kevin Y. Wu, Dania Akbar, Michel Giunta, Ananda Kalevar, Simon D. Tran

Bibliographic record

VenueMaterials · 2023
Typearticle
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsMcGill UniversityUniversity of TorontoUniversité de Sherbrooke
Fundersnot available
KeywordsSelf-healing hydrogelsDrug deliveryBiocompatibilityMedicinePosterior segment of eyeballNanotechnologyOphthalmologyMaterials science

Abstract

fetched live from OpenAlex

The human eye's intricate anatomical and physiological design necessitates tailored approaches for managing ocular diseases. Recent advancements in ophthalmology underscore the potential of hydrogels as a versatile therapeutic tool, owing to their biocompatibility, adaptability, and customizability. This review offers an exploration of hydrogel applications in ophthalmology over the past five years. Emphasis is placed on their role in optimized drug delivery for the posterior segment and advancements in intraocular lens technology. Hydrogels demonstrate the capacity for targeted, controlled, and sustained drug release in the posterior segment of the eye, potentially minimizing invasive interventions and enhancing patient outcomes. Furthermore, in intraocular lens domains, hydrogels showcase potential in post-operative drug delivery, disease sensing, and improved biocompatibility. However, while their promise is immense, most hydrogel-based studies remain preclinical, necessitating rigorous clinical evaluations. Patient-specific factors, potential complications, and the current nascent stage of research should inform their clinical application. In essence, the incorporation of hydrogels into ocular therapeutics represents a seminal convergence of material science and medicine, heralding advancements in patient-centric care within ophthalmology.

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)0.000
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 score0.306
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.092
GPT teacher head0.336
Teacher spread0.244 · 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

Citations23
Published2023
Admission routes1
Has abstractyes

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