Rates of Glaucomatous Visual Field Change in a Large Clinical Population
Bibliographic record
Abstract
PURPOSE: To determine the rate of glaucomatous visual field change in routine clinical care. METHODS: Mean deviation (MD) rate was computed in one randomly selected eye of all glaucoma patients and suspects with ≥5 examinations in a tertiary eye-care center. Proportions of "fast" (MD rate, <-1 to -2 dB/y) and "catastrophic" (<-2 dB/y) progressors were determined. The MD rates were computed in tertile groups by the number of examinations, baseline age, and MD. The MD rates were compared to the Canadian Glaucoma Study (CGS), a prospective study with IOP interventions mandated by visual field progression, by pairwise matching of patients by baseline MD. RESULTS: There were 2324 patients with median (interquartile range) baseline age and MD of 65 (56, 74) years and -2.44 (-5.44, -0.86) dB, and follow-up of 7.1 (4.8, 10.2) years with 8 (6, 11) examinations. The median MD rate was -0.05 (0.13, -0.30) dB/y, while the mean follow-up IOP was 17.1 (15.0, 19.7) mm Hg. The MD rate was progressively worse, with a doubling of fast and catastrophic progressors, with each tertile of increasing age. Worse MD rate was associated with lower follow-up IOP. Neither MD rate nor the number of fast and catastrophic progressors was significantly different in clinical care patients matched to CGS patients. CONCLUSIONS: Most patients under routine glaucoma care demonstrate slow rates of visual field progression. The MD rate in the current study was similar to an interventional prospective study, but considerably less negative compared to published studies with similar design.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".