Evaluation of contrast sensitivity in patients with advanced glaucoma: comparison of two tests
Bibliographic record
Abstract
Aims To evaluate contrast sensitivity (CS) in patients with advanced glaucomatous visual field damage, and to compare two clinical CS tests. Methods This was a cross-sectional test–retest study. Twenty-eight patients with open-angle glaucoma, visual acuity (VA) better than 20/40 and visual field mean deviation (MD) worse than −15 dB were enrolled. Patients underwent VA, visual field and CS testing with the Pelli-Robson (PR) chart and the Freiburg Visual Acuity and Contrast Test (FrACT). Retest measurements were obtained within 1 week to 1 month. Results Median (IQR) age and MD were 61.5 (55.5 to 69.2) years and −27.7 (−29.7 to −22.7) dB, respectively. Median (IQR) VA was 0.08 logarithm minimum angle of resolution (0.02 to 0.16), corresponding to 20/25 (20/20 to 20/30). Median (IQR) CS was 1.35 (1.11 to 1.51) log units with the PR chart and 1.39 (1.24 to 1.64) log units with FrACT. VA explained less than 40% of the variance in CS (adjusted R2=0.36). CS estimates of both tests were closely related (rho=0.88, p=0.001), but CS was 0.09 log units higher with FrACT compared with the PR chart, and the 95% repeatability intervals (Bland-Altman) were 46% tighter with the PR chart. Conclusions Despite near-normal VA, almost all patients showed moderate to profound deficits in CS. CS measurement provides additional information on central visual function in patients with advanced glaucoma.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".