Cognitive visual dysfunctions in preterm children with periventricular leukomalacia
Bibliographic record
Abstract
AIM: Cognitive visual dysfunctions (CVDs) reflect an impairment of the capacity to process visual information. The question of whether CVDs might be classifiable according to the nature and distribution of the underlying brain damage is an intriguing one in child neuropsychology. METHOD: We studied 22 children born preterm (12 males, 10 females; mean age at examination 8y, range 6-15y; mean gestational age 30wks, range 28-36wks) with periventricular leukomalacia, spastic diplegia, normal intelligence (mean Full-scale IQ 84; mean Verbal IQ 97; mean Performance IQ 74), and normal visual acuity, focusing on higher visual functions. Brain magnetic resonance images (MRI) were analysed to establish the presence of lesions along the primary optic pathway, in the occipitoparietal and occipitotemporal regions. RESULTS: Most children displayed an uneven cognitive profile, with deficits in visual object recognition, visual imagery, visual-spatial skills, and visual memory, and sparing of visual associative abilities, non-verbal intelligence, and face and letter recognition. Conventional brain MRI did not document major alterations of parietal and temporal white matter, or cortical alteration of areas involved in visual associative functions. INTERPRETATION: We suggest a widespread involvement of higher visual processing systems, involving both the ventral and dorsal streams, in preterm children with periventricular leukomalacia. The lack of major alterations on conventional MRI does not exclude the possibility of malfunctioning of higher visual processing systems, expressing itself through discrete CVDs. Possible mechanisms underlying these neuropsychological deficits are discussed.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".