Archetypal Analysis Reveals Consistent Visual Field Patterns for Stimulus Sizes III and V in Glaucoma and NAION
Bibliographic record
Abstract
Purpose: Disorders of the anterior optic nerve cause quantifiable patterns, or archetypes (AT), in visual fields (VFs) obtained using standardized automated perimetry using stimulus size III (size III). VFs with stimulus size V (size V) can reduce retest variability in eyes with moderate to severe loss. We postulated that VF testing using both stimuli would show similar ATs in eyes with glaucoma and nonarteritic anterior ischemic optic neuropathy (NAION). Methods: We used 1969 same-day pairs of 24-2 size III and size V VFs from two datasets. QRK207 is the largest NAION study to date, and the VIPII study measured same-day VFs across many stimulus sizes. We censored raw sensitivities of less than 21 dB for size III and 24 dB for size V and age-standardized to total deviations, before archetypal analysis (AA). We compared the ATs between the two stimuli and the combined data. Results: Using 14 ATs for both glaucoma and NAION, AA captured similar patterns between the two stimuli in both diseases with 87% of AT pairings having a cosine similarity of 0.8 or greater. The combined ATs retained the patterns in the separate stimuli VFs. Conclusions: AA shows that size V VFs provide quantifiable patterns of loss similar to size III. This aids in comparing stimulus sizes for monitoring VF patterns in disease progression. Translational Relevance: AA shows similar quantifiable patterns of VF loss with size III or size V, supporting the use of size V to monitor eyes with moderate to severe VF loss.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".