SU29. Are Visuospatial Working Memory Deficits in Schizophrenia Over Estimated due to the Presence of Visual Impairment? A Systematic Review
Bibliographic record
Abstract
Background: Visual deficits have been largely documented in people suffering from schizophrenia (SZ). These deficits may contribute to the low performance measured on visuospatial working memory (VSWM). It is important to establish whether the often-reported low working memory performance measured with neuropsychological tests is the by-product of visual deficits or the consequence of a genuine memory impairment. Our objective is to document if the potential impact of visual deficits on Spatial Span test—a well-known test used to assess VSWM—is taken into consideration by most researchers. A related objective is to distinguish between the different types of visual deficits and their relative impact on VSWM. Methods: A systematic review of the studies evaluating Spatial Span test performance in people suffering from SZ was realized in order to assess the content relative to vision. An exhaustive search of articles has been conducted in Medline, PsycNet and ProQuest databases and covered the period between 1980 and 2015. The inclusion criteria were (1) use of the Spatial Span task or a cognitive task similar to the Spatial Span task, (2) a group of people suffering from schizophrenia and a control group, and (3) a diagnosis made according to the DSM or ICD. A qualitative analysis was then performed on the data relative to the potential impact of visual deficits on VSWM tasks. Results: In total, 27 studies were included in the systematic review. The qualitative analysis revealed that the potential role of visual deficits in the execution of VSWM tasks in people suffering from SZ was mentioned in 9 papers. Among these studies, only 4 reported the proper evaluation of visual deficits. The results of the latter set of studies showed a correlational link between the visual deficits of people suffering from SZ and their performance on VSWM tasks. Conclusion: A small number of researchers seem to take into account the potential contribution of visual deficits to visuospatial working memory. The impact of visual deficits on the clinical neuropsychological assessment of cognitive functioning in SZ is currently not considered, which makes the assessment and identification of memory as a core deficit prone to biases.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".