How Best to Assess Suppression in Patients with High Anisometropia
Bibliographic record
Abstract
PURPOSE: We have recently described a rapid technique for measuring suppression using a dichoptic signal/noise task. Here, we report a modification of this technique that allows for accurate measurements to be made in amblyopic patients with high levels of anisometropia. This was necessary because aniseikonic image size differences between the two eyes can provide a cue for signal/noise segregation and, therefore, influence suppression measurement in these patients. METHODS: Suppression was measured using our original technique and with a modified technique whereby the size of the signal and noise elements was randomized across the stimulus to eliminate size differences as a cue for task performance. Eleven patients with anisometropic amblyopia, five with more than 5 diopters (D) spherical equivalent difference (SED), six with less than 5 D SED between the eyes, and 10 control observers completed suppression measurements using both techniques. RESULTS: Suppression measurements in controls and patients with less than 5 D SED were constant across the two techniques; however, patients with more than 5 D SED showed significantly stronger suppression on the modified technique with randomized element size. Measurements made with the modified technique correlated with the loss of visual acuity in the amblyopic eye and were in good agreement with previous reports using detailed psychophysical measurements. CONCLUSIONS: The signal/noise technique for measuring suppression can be applied to patients with high levels of anisometropia and aniseikonia if element size is randomized. In addition, deeper suppression is associated with a greater loss of visual acuity in patients with anisometropic amblyopia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".