Comparison of conventional and pattern discrimination perimetry in a prospective study of glaucoma patients.
Bibliographic record
Abstract
PURPOSE: To determine whether pattern discrimination perimetry detects progression of glaucomatous visual fields earlier than conventional static automated perimetry. METHODS: One hundred nine eyes of 109 patients with open angle glaucoma were enrolled in a longitudinal prospective study. Each patient underwent visual field examinations with conventional and pattern discrimination perimetry using the 30-2 program of the Humphrey Visual Field Analyzer (Humphrey Instruments Inc., San Leandro, CA) and a custom program for the pattern discrimination perimeter, respectively at 6-month intervals. Progression of glaucomatous visual field damage was assessed separately at each visit by predetermined criteria for conventional and pattern discrimination perimetry. The time to progression from baseline was calculated and the hemifield that showed progression first was documented for both conventional and pattern discrimination perimetry. RESULTS: Patients were followed for a mean of 5.1 years and a mean of 11.6 visits. Sixty-eight (62.3%) patients did not show progression with either technique. Of the remaining 41 patients, 15 (36.5%) showed progression with conventional perimetry alone, 9 (21.9%) with pattern discrimination perimetry alone, and 17 (41.4%) showed progression with both techniques. Of these 17 patients, 11 (64.7%) were detected earlier by conventional perimetry, and 6 (35.2%) were detected earlier by pattern discrimination perimetry. CONCLUSIONS: This study suggests that pattern discrimination perimetry is less effective than conventional perimetry in evaluating progressive glaucomatous visual field damage.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".