Improving event-based progression analysis in glaucomatous visual fields
Bibliographic record
Abstract
Glaucoma is a progressive optic neuropathy with characteristic changes to the optic nerve head and the visual field (VF). Detecting progression of VF damage with Standard Automated Perimetry (SAP) is of paramount importance for clinical care. One common approach to detecting progression is to compare each new VF test to a baseline SAP test (event analysis). This comparison is made difficult by the test-retest variability of SAP, which increases with the level of VF damage, and the limited range of measurement, meaning that damage cannot be assessed below a certain level. We performed a prospective international multi-centre data collection of SAP data on 90 eyes from 90 people with glaucoma and different levels of VF damage over a short period of time (6 tests in 60 days). Data were collected using a fundus tracked perimeter (Compass, CenterVue). We used these data (minus the first test) to develop an improved event analysis that accounts for both the change in variability with damage and the lower bound on the measurement imposed by SAP. Using simulations, we show that our approach is more sensitive compared to previously developed methods, especially in the case of advanced glaucoma, while retaining similar specificity.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".