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Record W1965727989 · doi:10.1016/j.jcrs.2005.08.060

Optical analysis of an accommodating intraocular lens

2006· article· en· W1965727989 on OpenAlexaffabout
Jennifer J. Hunter, Melanie C. W. Campbell, Edward Geraghty

Bibliographic record

VenueJournal of Cataract & Refractive Surgery · 2006
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsJDSU (Canada)University of Waterloo
Fundersnot available
KeywordsAccommodationLens (geology)Intraocular lensOptometryOpticsEntrance pupilPoint (geometry)PupilComputer scienceOphthalmologyIntraocular lensesImage qualityComputer visionArtificial intelligenceMedicineImage (mathematics)MathematicsPhysicsGeometry

Abstract

fetched live from OpenAlex

PURPOSE: To model the feasibility of an accommodating intraocular lens (IOL) that allows near vision by means of an anterior translation within the capsular bag. SETTING: University of Waterloo, Waterloo, Ontario, Canada. METHODS: Model eyes were constructed and analyzed based on experimental data using Code V, a computerized optical design tool. The potential near vision of IOLs of different powers was calculated as they were moved anteriorly within the capsular bag. The conditions under which a spherical lens performs well and when an aspheric design should be considered were determined. RESULTS: Accommodation (the dioptric change from the far to the near point) varies linearly with lens movement and is sensitive to corneal and IOL powers. Simple equations were derived and accurately predicted induced accommodation. Retinal image quality varies significantly with pupil size and IOL power. However, image quality is minimally affected by the amount of induced accommodation. CONCLUSIONS: From an optical standpoint accommodation with adequate image quality can be achieved by anterior movement of a spherical IOL within the capsular bag.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.036
GPT teacher head0.359
Teacher spread0.323 · 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 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
Published2006
Admission routes2
Has abstractyes

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