Clinical Significance of Optic Disc Progression by Topographic Change Analysis Maps in Glaucoma: An 8-Year Follow-Up Study
Bibliographic record
Abstract
Aim. To investigate the ability of Heidelberg Retina Tomograph (HRT3) Topographic Change Analysis (TCA) map to predict the subsequent development of clinical change, in patients with glaucoma. Materials. 61 eyes of 61 patients, which, from a retrospective review were defined as stable on optic nerve head (ONH) stereophotographs and visual field (VF), were enrolled in a prospective study. Eyes were classified as TCA-stable or TCA-progressed based on the TCA map. All patients underwent HRT3, VF, and ONH stereophotography at 9-12 months intervals. Clinical glaucoma progression was determined by masked assessment of ONH stereophotographs and VF Guided Progression Analysis. Results. The median (IQR) total HRT follow-up period was 8.1 (7.3, 9.1) years, which included a median retrospective and prospective follow-up time of 3.9 (3.1, 5.0) and 4.0 (3.5, 4.7) years, respectively. In the TCA-stable eyes, VF and/or photographic progression occurred in 5/13 (38.4%) eyes compared to 11/48 (22.9%) of the TCA-progressed eyes. There was no statistically significant association between TCA progression and clinically relevant (photographic and/or VF) progression (hazard ratio, 1.18; P = 0.762). The observed median time to clinical progression from enrollment was significantly shorter in the TCA-progressed group compared to the TCA-stable group (P = 0.04). Conclusion. Our results indicate that the commercially available TCA progression criteria do not adequately predict subsequent photographic and/or VF progression.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".