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Record W4408336077 · doi:10.2147/opth.s512246

The Impact of Comfort Eluting Agents and Replacement Frequency on Enhancing Contact Lens Performance

2025· review· en· W4408336077 on OpenAlexaff
Chau‐Minh Phan, Alex Hui, Xinfeng Shi, Ying Zheng, Lakshman N. Subbaraman, James Yuliang Wu, Lyndon Jones

Bibliographic record

VenueClinical ophthalmology · 2025
Typereview
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMedicineContact lensOphthalmologyOptometryBiomedical engineering

Abstract

fetched live from OpenAlex

This review explores the development and clinical implications of soft contact lenses designed to elute comfort agents, emphasizing their role in enhancing user experience and ocular health. As discomfort remains one of the primary reasons for discontinuation of lens wear, this concept aims to address this challenge by gradually releasing these agents over their period of use. This review also explores the effectiveness, safety, and user satisfaction associated with frequent replacement schedules of these lenses. Clinical trials demonstrate that lenses with eluting comfort agents significantly reduce dryness and irritation, leading to improved wear-time and overall comfort. The findings suggest that frequent replacement not only enhances lens hygiene but also maximizes the therapeutic benefits of the eluted agents, promoting a healthier ocular environment. The implications for practice highlight a shift towards more patient-centered approaches in contact lens design and management, aiming to improve adherence and satisfaction among users. This research paves the way for future innovations in contact lens technology, focusing on personalized solutions that cater to individual comfort needs.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.118
GPT teacher head0.468
Teacher spread0.350 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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