MétaCan
Menu
Back to cohort
Record W4392885209 · doi:10.1002/smo.20230021

Smart molecules in ophthalmology: Hydrogels as responsive systems for ophthalmic applications

2024· review· en· W4392885209 on OpenAlex
Merve Kulbay, Kevin Y. Wu, Doanh Truong, Simon D. Tran

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSmart Molecules · 2024
Typereview
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsUniversité de SherbrookeMcGill University
Fundersnot available
KeywordsSelf-healing hydrogelsDrug deliveryNanotechnologyTransformative learningComputer scienceMaterials sciencePsychology

Abstract

fetched live from OpenAlex

This comprehensive review delves into a unique intersection of hydrogels as smart molecules and their transformative applications in ophthalmology. Beginning with the foundational definition, properties, and classification of hydrogels, the review explores their synthesis and responsive capabilities. Specific applications examined encompass topical drug delivery, contact lenses, intravitreal drug delivery, ocular adhesives, vitreous substitutes, and cell-based therapy. A methodical analysis, including an overview of relevant ocular structures and a comparative evaluation of hydrogel-based solutions against traditional treatments, is conducted. Additionally, potential constraints, translation challenges, knowledge gaps, and research areas are identified. Our methodical approach, guided by an extensive literature review from 2017 to 2023, illuminates the unprecedented opportunities offered by hydrogels, along with pinpointing areas for further inquiry to facilitate their transition into clinical practice.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.043
GPT teacher head0.363
Teacher spread0.320 · 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