Feasibility, Accuracy, and Repeatability of Suprathreshold Saccadic Vector Optokinetic Perimetry
Bibliographic record
Abstract
PURPOSE: To evaluate feasibility, accuracy, and repeatability of suprathreshold Saccadic Vector Optokinetic Perimetry (SVOP) by comparison with Humphrey Field Analyzer (HFA) perimetry. METHODS: = 30, age 16-61 years). The test protocol comprised repeat suprathreshold SVOP and HFA testing with the C-40 test pattern. Feasibility was assessed by protocol completeness. Sensitivity, specificity, and accuracy of SVOP was established by comparison with reliable HFA tests in two ways: (1) visual field pattern results (normal/abnormal), and (2) individual test point outcomes (seen/unseen). Repeatability of each test type was assessed using Cohen's kappa coefficient. RESULTS: Of subjects, 82% completed a full protocol. Poor reliability of HFA testing in child patients limited the robustness of comparisons in this group. Sensitivity, specificity, and accuracy across all groups when analyzing the visual field pattern results was 90.9%, 88.5%, and 89.0%, respectively, and was 69.1%, 96.9%, and 95.0%, respectively, when analyzing the individual test points. Cohen's kappa coefficient for repeatability of SVOP and HFA was excellent (0.87 and 0.88, respectively) when assessing visual field pattern results, and substantial (0.62 and 0.74, respectively) when assessing test point outcomes. CONCLUSIONS: SVOP was accurate in this group of adults. Further studies are required to assess SVOP in child patient groups. TRANSLATIONAL RELEVANCE: SVOP technology is still in its infancy but is used in a number of centers. It will undergo iterative improvements and this study provides a benchmark for future iterations.
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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 | 0.008 | 0.027 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".