Are High-Pass Resolution Perimetry Thresholds Sampling Limited or Optically Limited?
Bibliographic record
Abstract
PURPOSE: It has been reported that high-pass resolution perimetry (HRP) provides a means of noninvasively determining retinal ganglion cell density. However, there is evidence to suggest that this may not be true. The purpose of the present study was to determine whether HRP thresholds are sampling limited, which is a necessary condition for being able to determine retinal ganglion cell density psychophysically. METHODS: This study measured resolution and detection performance for a range of grating-based stimuli under the testing conditions that HRP uses and compared these with performance of the ring stimulus. RESULTS: The results show that detection and resolution acuity under HRP test conditions were often equivalent, in accordance with previous investigations. However, the results also show that the thresholds underestimated the true level of resolution acuity in the periphery because increasing stimulus contrast increased performance. CONCLUSION: These findings suggest that HRP thresholds cannot be regarded as sampling limited, but rather they are optically limited. We therefore conclude that HRP thresholds cannot be regarded as a direct measure of the underlying ganglion cell density.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".