A new method to measure the spatial frequency dependency of interocular suppression in amblyopia
Bibliographic record
Abstract
Objective Using a standard dichoptic noise-masking paradigm combined with qCSF method to assess the inter ocular suppression at different spatial frequencies in adults with amblyopia normal controls. Methods Ten adults with anisometropia amblyopia and 17 normal controls participated this research study. The degree of interocular suppression was quantified by the threshold elevation when the untested eye viewed a band-pass filtered noise compared to that when the untested eye viewed an unstructured field of the same mean luminance. Monocular signal attenuation by the amblyopic eye was accounted for by setting the contrast of the noise mask to a 5 times of its detection threshold. Data were analyzed using repeated measured ANOVA. Results This method was efficient in measuring the interocular suppression in both amblyopes and normal controls. We also found symmetric interocular suppression for normals (F=0.32, P>0.05), but asymmetric interocular suppression for amblyopes (F=24.25, P 0.05). Conclusion Amblyopia involves an asymmetric interocular inhibition not accounted for by the signal attenuation of the amblyopic eye. This imbalance leads to dominance by the fellow eye over the amblyopic eye under binocular viewing (i.e. suppression). Key words: Amblyopia; Interocular suppression; Quick contrast sensitivity function; Noise masking
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".