A comparative study of the efficiency of chart versus computer-generated contrast sensitivity testing in glaucoma patients and controls
Bibliographic record
Abstract
Purpose. The goal of this study was to assess the efficiency of chart vs. computergenerated contrast sensitivity tests in glaucoma patients and controls. Methods. A total of 64 individuals (30 young controls, 18 older controls, 16 glaucoma patients) were tested for contrast sensitivity using 4 different tests. Two tests determined contrast sensitivity (CS) for detecting large targets with sharp borders. One of these was the MARS printed chart, and the other a computerized number search test by Bailey. The second assessment determined spatial contrast sensitivity (SCS) for sinusoidal grating targets at several spatial frequencies. One of these was the printed Vistech chart, the other a computerized test by Faubert. Results. Both CS tests showed a decrease in the glaucoma group versus both the control groups (p < 0.001). The tests for SCS demonstrated a decrease in sensitivity both with age (p < 0.001) and in the presence of glaucoma (p < 0.001) across all spatial frequencies. Conclusion. The data indicated that SCS was superior in separating the three study groups. Neither of the computer-generated tests was more sensitive than its printed counterpart.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".