Research on schizophrenia – based on dementia, working memory and episodic memory deficits
Bibliographic record
Abstract
Schizophrenia is associated with severe cognitive dysfunctions, including memory deficits that affect both working memory (WM) and episodic memory (EM). Memory deficits are also a typical symptom experienced by dementia patients that causes crucial functional problems in daily life. Meanwhile, past studies yielded evidence for the relationship between dementia and psychiatric disorders, indicating the emergence of psychotic symptoms during prodromal dementia. This article aims to investigate the relationship between schizophrenia and dementia plus the WM and EM deficits among schizophrenia patients. In the first part of this paper, the association between schizophrenia and dementia is examined based on a psychosocial study conducted among the Danish population, comparing the likelihood of developing dementia in patients with schizophrenia to that of individuals without schizophrenia. The review of previous research findings indicates a positive correlation between a diagnosis of schizophrenia and the later development of dementia. This phenomenon may be due to the reason that schizophrenia is positively associated with several established risk factors of dementia. Then, the topic of the potential deficits of WM and EM is discussed using self-ordered task results and neurobiological evidence. Studies have showed that schizophrenia is likely to lead significant WM and EM limitations that are closely related to prefrontal cortex impairment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".