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Record W4411216296 · doi:10.1039/9781839169779-00048

Intraocular Lenses

2025· book-chapter· en· W4411216296 on OpenAlexaff
Samina Karim, Laura A. Wells

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsQueen's University
Fundersnot available
KeywordsIntraocular lensesOptometryOphthalmologyMedicineIntraocular lens

Abstract

fetched live from OpenAlex

Cataracts are a prevalent ocular disease that are treated by removing the diseased lens and replacing it with a prosthetic known as an intraocular lens (IOL). IOLs are transparent polymers designed to replace the function of a lens. However, some postoperative complications result in the return of vision loss, encouraging research to continually develop new strategies to improve IOLs. Advances in IOL technology, drug-delivery systems, and surgical procedures are focused on enhancing the integration of the implanted IOL to improve functionality. There is a growing focus on tuning/modulating material properties of commonly used IOLs with surface modifications to direct the biological response of the surrounding tissue. This chapter will provide an overview of cataracts, the pathology of post-operative complications, the evolution of IOL materials, and the ongoing research to fulfill the challenges present with current IOLs.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.775
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0110.001

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 head0.014
GPT teacher head0.241
Teacher spread0.227 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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