Simulation of longitudinal threshold visual field data.
Bibliographic record
Abstract
PURPOSE: To describe and evaluate a computer model that simulates longitudinal visual field data. METHODS: A computer model was designed using factors that influence thresholds of normal and glaucomatous visual fields. The simulation model was used to quantify the effects of fluctuation on the outcomes of pointwise linear regression by comparison with simulated gold standard data with no variability. RESULTS: Serial sets of 10 stable and 10 progressive visual fields with different fluctuation levels were generated by simulation and were analyzed using pointwise linear regression. Regression outcome measures used were slopes of -1 dB/year or worse and slopes of -1 dB/year or worse that were also statistically significant. In stable visual fields, the number of locations with regression slopes worse than -1 dB/year increased with fluctuation and defect size and was inversely related to the number of fields. The number of locations with statistically significant slopes remained low and appeared unaffected by these variables. In progressive visual fields, analysis of a small number of visual field test results (<8) overestimated the number of locations with regression slopes worse than -1 dB/year and underestimated the number of locations with statistically significant slopes. CONCLUSIONS: Computer simulation may be used to provide a gold standard outcome that permits evaluation of statistical tools for monitoring progressive glaucomatous visual field loss.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".