Effect of Induced Refractive Error and Nuclear Sclerotic Cataracts on Ishihara Colour Plate Testing
Bibliographic record
Abstract
Objective: To determine the effect of induced refractive blur and nuclear sclerotic (NS) cataracts on Ishihara colour plate (ICP) scores.Design: Prospective evaluation of a diagnostic test Participants: Patients who presented to Hotel Dieu Hospital Eye clinic between January and March 2010 with either a lone diagnosis of nuclear sclerotic cataracts, or with no identified ocular disease with complete examination.Methods: Patients were divided into two groups: those having no identified ocular disease on examination, and those with a lone diagnosis of NS cataracts.The first group was refractively blurred with +3D, +6D, +9D and +12D lenses.Best corrected visual acuity (BCVA) at near and ICP scores was obtained from both groups.Results: There were 20 patients in each group.BCVA at near and ICP values declined with increasing amounts of plus lenses as expected.With the application of the +3D, +6D, +9D, and +12D lenses, the association between ICP scores and BCVA at near decreased (-0.845, -0.678, -0.374, and -0.363 respectively).There was little clinical decline in absolute ICP scores until lenses of +9D (mean BCVA at near = 20/400) or greater were applied.The correlation between ICP scores and BCVA at near declines due to NS cataracts was -0.53.Conclusion: ICP testing is valid to BCVA at near of 20/100 when the acuity decline is due solely to NS cataracts.Further study is required to determine whether or not severe vision loss due to NS cataracts affects ICP scores and at what visual acuity.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".