Bibliographic record
Abstract
Cerebral visual disorders include a range of common and rare deficits. They can be divided into effects on low-, intermediate-, and high-level forms of visual processing. Low-level deficits are various forms of homonymous hemifield scotomata, which affect all types of vision within their borders. Intermediate-level deficits refer to impairments of colour or motion perception, which affect either one hemifield or the entire field when lesions are bilateral. High-level deficits are divided into those of the ventral (occipitotemporal) or dorsal (occipitoparietal) stream. Occipitotemporal lesions affect various aspects of object recognition, ranging from general visual agnosia to selective agnosias, such as prosopagnosia or topographagnosia from right or bilateral lesions, and pure alexia from left-sided lesions. Occipitoparietal lesions cause the various components of Bálint syndrome, namely, simultanagnosia, optic ataxia, and ocular motor apraxia. They can also cause other impairments of visuospatial or visuotemporal processing, such as astereopsis and sequence-agnosia. Because of anatomic proximity, certain deficits cluster together to form a number of cerebral visual syndromes. Treatment of these disorders remains challenging, with frequent reliance on strategic substitutions rather than restorative approaches.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".