Multiple (frontloaded) visual field tests increase identification of very slow mean deviation progression in glaucoma
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of frontloading (multiple) visual field (VF) tests at the same visit for detecting mean deviation (MD) change in slowly progressive glaucoma. METHODS: This was a computer simulation study. Baseline MD (range, 0 to -12 dB) and progression rate (range, 0 to -0.4 dB/year, non-inclusive) were generated for 10,000 patients. Each patient had 6 simulated "stable" baseline VF tests. Then follow-up VFs (up to 10 years) were generated by incorporating progression rate and within-visit and between-visit variability. The independent variables were number of VF tests per visit (one non-frontloaded or two frontloaded), VF reliability (100%, 85%, or 70%), repeat testing because of unreliable results (yes or no), and follow-up interval (6-monthly or yearly). The outcomes were detection of progression (MD slope that was negative and significant at p < 0.05), MD at detection, and number of years to detection. RESULTS: Frontloading identified more progressors (62.7%-79.2%) compared with non-frontloading (31.0%-36.7%) at 10 years (p < 0.0001). Six-monthly follow-ups led to greater detection than yearly intervals. Progressors detected by both methods were detected by the non-frontloaded method sooner (up to 0.26 years), but this was small and not clinically significant (MD difference, 0.06 dB). An increase (less severe) in MD, an increase (slower) in progression rate, and an increase in SD of baseline VFs decreased the likelihood of detecting progression. CONCLUSIONS: Frontloading VF tests at 6-monthly intervals improve detection rates of MD progression in slowly progressive glaucoma patients compared with performing 1 test per visit at yearly intervals.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".