Defining glaucomatous optic neuropathy using objective criteria from structural and functional testing
Bibliographic record
Abstract
BACKGROUND/AIMS: To identify objective criteria from optical coherence tomography (OCT) and perimetry that denote a useful, specific definition of glaucomatous optic neuropathy (GON) in eyes with open-angle glaucoma for comparisons among glaucoma research studies. METHODS: A cross-sectional study of adult patients with glaucoma from nine centres on five continents evaluated de-identified physician diagnosis, OCT and perimetry results for 2580 eyes (1531 patients) in an online database. Each eye was graded by their glaucoma specialist as either definite, probable or not GON. Objective measures from OCT and perimetry, derived from an online consensus panel comprising 176 glaucoma specialists globally, were compared against the three diagnostic levels. RESULTS: Diagnoses were 54% 'definite', 22% 'probable' and 24% 'not GON'. Using only OCT data or only field data had inadequate specificity (<90%). The best definitional choice for data from either the most recent or the preceding OCT/field pair had 77% sensitivity at 98% specificity and consisted of abnormal OCT superior or inferior nerve fibre layer quadrant with matching, opposite, abnormal Glaucoma Hemifield Test. CONCLUSIONS: Objective criteria to define GON are practical and may be useful for comparisons among clinical studies to supplement subjective clinical assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.002 | 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".