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Record W4399748956 · doi:10.3390/polym16121717

Biopolymeric Innovations in Ophthalmic Surgery: Enhancing Devices and Drug Delivery Systems

2024· article· en· W4399748956 on OpenAlexaff
Kevin Y. Wu, Sameer Khan, Zhuoying Liao, Michael Marchand, Simon D. Tran

Bibliographic record

VenuePolymers · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicOcular Disorders and Treatments
Canadian institutionsMcGill UniversityMcMaster UniversityCarleton UniversityUniversité de Sherbrooke
Fundersnot available
KeywordsOphthalmic surgeryDrug deliveryBiocompatibilityNanotechnologyMedicineBiocompatible materialMedical physicsBiomedical engineeringMaterials scienceSurgery

Abstract

fetched live from OpenAlex

The interface between material science and ophthalmic medicine is witnessing significant advances with the introduction of biopolymers in medical device fabrication. This review discusses the impact of biopolymers on the development of ophthalmic devices, such as intraocular lenses, stents, and various prosthetics. Biopolymers are emerging as superior alternatives due to their biocompatibility, mechanical robustness, and biodegradability, presenting an advance over traditional materials with respect to patient comfort and environmental considerations. We explore the spectrum of biopolymers used in ophthalmic devices and evaluate their physical properties, compatibility with biological tissues, and clinical performances. Specific applications in oculoplastic and orbital surgeries, hydrogel applications in ocular therapeutics, and polymeric drug delivery systems for a range of ophthalmic conditions were reviewed. We also anticipate future directions and identify challenges in the field, advocating for a collaborative approach between material science and ophthalmic practice to foster innovative, patient-focused treatments. This synthesis aims to reinforce the potential of biopolymers to improve ophthalmic device technology and enhance clinical outcomes.

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 score0.170
Threshold uncertainty score0.434

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 head0.008
GPT teacher head0.239
Teacher spread0.231 · 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

Citations8
Published2024
Admission routes1
Has abstractyes

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