Visual Performance in the “Real World”: Contrast Sensitivity, Visual Acuity, and Effects of Macular Carotenoids
Bibliographic record
Abstract
Visual acuity (VA) is compared to contrast sensitivity (CS) testing in assessing "real-world" visual performance, and it is recommended that both should be measured routinely in the clinic. The role of nutritional intervention in improving visual performance is reviewed and emphasized. A brief history and illustration of both VA and CS, within the scope of visual performance, is presented. Parameters for effective CS testing in the clinic, and guidelines for interpretation of results, including a new model for understanding the visual impact of changes in CS, are also presented. Relevant research that supports the use of the macular carotenoids lutein, zeaxanthin, and meso-zeaxanthin to enhance visual performance is reviewed with suggested guidelines for supplementation. CS testing is easily performed at a single intermediate target size and is an excellent tool for the accurate assessment of a patient's overall visual experience. Research continues to uncover the strong link between nutrition and visual performance; the macular carotenoids appear to be especially effective in this regard, and their benefits to visual performance now importantly include contrast sensitivity. Clinicians can provide an improved level of care by incorporating into the examination protocol CS testing and, where appropriate, nutritional counseling and intervention.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".