Toward a new definition of glaucomatous optic neuropathy for clinical research
Bibliographic record
Abstract
PURPOSE OF REVIEW: A process is ongoing to produce a definition of glaucomatous optic neuropathy (GON) using quantitative, objective data from structural and functional tests. At present, a common practice is to define GON by subjective features said to be 'characteristic' as judged by those experienced in glaucoma care. RECENT FINDINGS: An objective definition would standardize the comparison of clinical research results across studies, without precluding simultaneous use of idiosyncratic definitions in the same reports. To achieve this goal, expert opinion was solicited to reach optimal agreement on one or more consensus, GON definitions. An interactive period of online discussion by 176 international experts led to 110 responses in an online survey that narrowed possible definitional structures into testable criteria. SUMMARY: Two approaches to validation of one or more sets of criteria for definite and possible GON are ongoing. The general principles include definition for each eye individually, inclusion of a borderline category, no intraocular pressure criterion, and both structural and functional defects in appropriate physical locations. Each validation approach uses clinician diagnosis as a standard against which objective criteria are compared, with the initial approach using a three-level categorical scale, and the second approach using 0--100 scaling.
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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 | 0.032 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".