Relating self‐disorders to neurocognitive and psychopathological measures in first‐episode schizophrenia
Bibliographic record
Abstract
AIM: The notion of a disturbed self as the core feature of schizophrenia dates back to the founding texts on the illness. Since the development of the psychometric tool for examination of anomalous self-experience (EASE), self-disorders have become accessible to empirical research. Empirical studies have shown that EASE measured self-disorders predict schizophrenia spectrum in prospective studies and consistently show a selective hyper aggregation of self-disorder in schizophrenia and schizotypal disorders. The aim of this study is to investigate the relationship between self-disorders cognitive deficits and symptoms in schizophrenia. METHODS: Thirty-five non-acute first-episode patients with schizophrenia and 35 matched healthy controls were evaluated with EASE, cognitive deficits, and symptoms (PANSS positive, negative and general). [Correction added on 28 January 2022, after first online publication: the words, 'evaluated with' were missing and have now been added to the preceding sentence.] RESULTS: The results show that self-disorders and symptoms are correlated among patients with schizophrenia, but not with cognitive deficits. Moreover, with the exception of attentional deficits, neurocognitive impairment was not significantly higher among patients with schizophrenia compared to healthy controls. CONCLUSIONS: We argue that this adds support to a view of schizophrenia as being characterized by specific traits of pre-reflective self-disturbance, which are related to the severity of symptoms, whereas neurocognitive impairment reflects a separate or distinct aspect of schizophrenia.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".