Insights from the examination of verbal and spatial memory errors in relation to clinical symptoms of patients with recent-onset schizophrenia
Bibliographic record
Abstract
INTRODUCTION: Memory deficits in patients with schizophrenia (SZ) are considered as a key feature of the clinical manifestations of the disease. In order to further examine the role and nature of memory deficits in SZ, the pattern of errors in verbal and spatial serial recall tasks committed by SZ patients was compared to that of healthy controls. We also tested the relationship between these memory errors and clinical symptoms. METHODS: Twenty-seven outpatients with recent-onset SZ and 27 age and gender matched healthy controls had to remember sequences of items (digits or localisations) in a serial recall task. Clinical symptoms were assessed with the PANSS and the SAPS. RESULTS: The results indicate that the number of omissions, intrusions, and transpositions can differentiate patients with SZ from healthy controls. Intrusions and transpositions committed in the verbal domain were associated with the negative subscale of the PANSS. Transposition errors were associated with delusions whether the to-be-remembered information was verbal or spatial. CONCLUSION: The examination of the pattern of errors, in particular that of transpositions, is a more informative cognitive index than the mere analysis of overall performance, and provides a promising target for treatment.
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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) | 0.000 | 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) | 0.001 | 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".