Aniseikonia and anisometropia: implications for suppression and amblyopia
Bibliographic record
Abstract
Aniseikonia is a difference in the perceived size or shape of images between eyes, and can arise from a variety of physiological, neurological, retinal, and optical causes. Aniseikonia is associated with anisometropia, as both anisometropia itself and the optical correction for anisometropia can cause aniseikonia. Image size differences above one to three per cent can be clinically symptomatic. Common symptoms include asthenopia, headache and diplopia in vertical gaze. Size differences of three and more impair binocular visual functions such as binocular summation and stereopsis. Above five per cent of aniseikonia, binocular inhibition or suppression tend to occur to prevent diplopia and confusion. Aniseikonia can be measured using a range of techniques and can be corrected or reduced by prescribing contact lenses or specially designed spectacle lenses. Subjective testing of aniseikonia is the only way to accurately measure the overall perceived amount of aniseikonia. However, currently it is not routinely assessed in most clinical settings. At least two-thirds of patients with amblyopia have anisometropia, thus we may expect aniseikonia to be common in patients with anisometropic amblyopia. However, aniseikonia may not be experienced by the patient under normal binocular viewing conditions if the image from the amblyopic eye is of poor quality or is too strongly suppressed for image size differences to be recognised. This lack of binocular simultaneous perception in amblyopia may also prevent the measurement of aniseikonia, as most common techniques require direct comparisons of images seen by each eye. Current guidelines for the treatment of amblyopia advocate full correction of anisometropia to equalise image clarity, but do not address aniseikonia. Significant image size differences between eyes may lead to suppression and abnormal binocular adaptations. It is possible that correcting anisometropia and aniseikonia simultaneously, particularly at the initial diagnosis of anisometropia, would reduce the need to develop suppression and improve treatment outcomes for 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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".