Sensitivity Differences Between Real-Patient and Computer-Stimulated Visual Fields
Bibliographic record
Abstract
PURPOSE: The authors sought to verify computer simulation of visual fields by comparing thresholds of real and corresponding simulated visual fields. METHODS: Four patients with stable glaucomatous visual fields and three patients with progressing glaucomatous visual fields were chosen for the study. Visual fields had been recorded at 6-month intervals for 5 to 7.5 years. A previously described computer simulation program was used to generate a corresponding simulated visual field for each of the real fields. Twenty different levels of response variability and long-term variability were used in the simulations. Pointwise sensitivity differences between real and simulated fields were calculated. The average difference and 95% interval of the differences were analyzed for the different simulation conditions, for the pointwise sensitivities in the real patient fields, and to determine whether the field was stable or progressing. RESULTS: In almost all simulation conditions, the average pointwise sensitivity differences ranged from -1 to 1 dB and were not significantly different among different simulation conditions. The 95% interval of the average difference increased significantly with response variability, whereas long-term variability failed to show any apparent effect. Average pointwise differences and the 95% intervals were greatest in locations where the real-patient field had reduced sensitivity of 14 dB or worse. CONCLUSION: The simulation program provided good estimates of visual field sensitivities. Increasing amounts of response, but not long-term variability, produced a linear increase in the variability of threshold sensitivities. This finding implies that short-term rather than long-term fluctuation is the most important factor determining the variability of thresholds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".