Luminance contrast with clear and yellow-tinted intraocular lenses
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".