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Record W2017459596 · doi:10.1016/j.jcrs.2007.03.024

Luminance contrast with clear and yellow-tinted intraocular lenses

2007· article· en· W2017459596 on OpenAlexaffabout
Andréna Pierre, Walter Wittich, Jocelyn Faubert, Olga Overbury

Bibliographic record

VenueJournal of Cataract & Refractive Surgery · 2007
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsMesopic visionLuminancePhotopic visionOphthalmologyMedicineContrast (vision)OptometryIntraocular lensVisual acuityOpticsRetinal

Abstract

fetched live from OpenAlex

PURPOSE: To determine whether yellow-tinted intraocular lenses (IOLs) negatively affect luminance contrast in postoperative cataract patients. SETTING: Department of Ophthalmology, Sir Mortimer B. Davis Jewish General Hospital, McGill University, Montreal, Quebec, Canada. METHODS: Luminance contrast was measured using the minimum-motion technique. The stimulus consisted of blue and red sinusoidal gratings differing in luminance. Patients had implantation of a clear or yellow-tinted IOL and were tested monocularly 2 to 9 weeks after cataract surgery. No patient had concomitant ocular diseases or congenital color defects, assessed by their ophthalmologist, or flicker-sensitive epilepsy. All patients had a visual acuity of 20/40 or better a mean of 4 weeks+/-2 (SD) postoperatively. RESULTS: Patients ranged in age from 55 to 89 years. An independent-samples Student t test showed that patients with a yellow-tinted IOL had significantly lower luminance contrast values than patients with a clear IOL (P<.05). CONCLUSIONS: The results suggest that yellow-tinted IOLs affect the perception of luminance under photopic conditions. More blue light was required to make luminance judgments with a yellow-tinted IOL than with a clear IOL. Further study of the functional impact of luminance reduction by yellow-tinted IOLs is warranted.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.019
GPT teacher head0.317
Teacher spread0.298 · 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 designObservational
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

Citations37
Published2007
Admission routes2
Has abstractyes

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