Longitudinal and Cross-sectional Analyses of Visual Field Progression in Participants of the Ocular Hypertension Treatment Study
Bibliographic record
Abstract
OBJECTIVE: To assess agreement between longitudinal and cross-sectional analyses for determining visual field progression in data from the Ocular Hypertension Treatment Study. METHODS: Visual field data from 3088 eyes of 1570 participants (median follow-up, 7 years) were analyzed. Longitudinal analyses were performed using change probability with total and pattern deviation, and cross-sectional analyses were performed using the glaucoma hemifield test, corrected pattern standard deviation, and mean deviation. The rates of mean deviation and general height change were compared to estimate the degree of diffuse loss in emerging glaucoma. RESULTS: Agreement on progression in longitudinal and cross-sectional analyses ranged from 50% to 61% and remained nearly constant across a wide range of criteria. In contrast, agreement on absence of progression ranged from 97.0% to 99.7%, being highest for the stricter criteria. Analyses of pattern deviation were more conservative than analyses of total deviation, with a 3 to 5 times lesser incidence of progression. Most participants developing field loss had both diffuse and focal changes. CONCLUSIONS: Despite considerable overall agreement, 40% to 50% of eyes identified as having progressed with either longitudinal or cross-sectional analyses were identified with only one of the analyses. Because diffuse change is part of early glaucomatous damage, pattern deviation analyses may underestimate progression in patients with ocular hypertension.
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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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".