Properties of the Statpac Visual Field Index
Bibliographic record
Abstract
Purpose. To compare the properties of the visual field index (VFI) to those of mean deviation (MD) in patients with glaucoma. Methods. MD and VFI were calculated in data obtained from an ongoing longitudinal study in which patients with glaucoma (N = 109, 204 eyes) were observed for 9.8 years (median, 21 tests) with static automated perimetry. MD and VFI were compared in one test of each eye, and a subset of 30 tests were selected to compare the VFI with the judgments of eight experts who judged the percentage of the remaining visual field. In series of tests obtained over time, rates of change, statistical significance, evidence of nonlinearity, and variability were compared between both indices. Results. In single tests, MD and VFI were closely related (r = 0.88, P < 0.001). The relationship between both indices appeared linear, except in visual fields with MDs better than -5.0 dB where 29 (22%) of 129 eyes exhibited a ceiling effect (VFI = 100%). Based on this relationship, the predicted VFIs for visual fields with MDs of -5, -10, and -15 dB were 91%, 76%, and 60%, respectively. The percentage of remaining visual field suggested by the VFI exceeded the range of the experts' subjective judgments in 16 (53%) of 30 eyes. In series of tests obtained over time, rates of change with the two indices were closely related (r = 0.79, P < 0.001), and statistically significant reductions over time (P < 0.05) occurred in a similar number of eyes (92 [45%] with MD, and 87 [43%] with VFI). Of the 105 eyes with statistically significant (P < 0.05) negative trend in either MD or VFI, 74 (70%) showed such trends with both indices (κ = 0.69). The variability of MD and VFI increased with damage, and there was no evidence that change over time was more linear with VFI than with MD. Conclusions. The VFI provides a simple and understandable metric of visual field damage, but its estimates of remaining visual field were more optimistic than those of the experts. Rates of change over time with both indices were closely related, but the reliance of the VFI on pattern deviation probability maps caused a ceiling effect that may have reduced its sensitivity to change in eyes with early damage. In this group of patients there was no evidence to suggest that the VFI is either superior or inferior to the MD as a summary measure of visual field damage.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".