An Electrophysiologic Study: Can Semantic Context Processes Be Mobilized in Patients with Thought-Disordered Schizophrenia?
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to test the hypothesis that reinforcing the structuring of verbal material may bring about an improvement in contextualization processes in patients with formal thought-disordered schizophrenia. METHOD: A total of 38 schizophrenia patients with formal thought disorders and 24 matched healthy control subjects performed 2 lexical decision tasks, involving 2 levels of contextual structuring (with 16.7% and 33% of related words, respectively). The event-related potentials, N400 and late positive component (LPC), and behavioural variables (reaction times and error percentages) were analyzed. RESULTS: A context-structuring effect was observed on LPC, but not on N400. In subjects with schizophrenia, the N400 anomalies (that is, increase in amplitude for the related words and reduction of the N400 effect) persisted in both context-structuring conditions. Similarly, a reduction in LPC amplitude for the unrelated word category, as well as a decrease in the LPC effect, was observed in these patients. CONCLUSIONS: The schizophrenia patients with formal thought disorders did not benefit from the structuring of the context to implement context integration strategies. This deficit appears to be stable. The results are discussed within the framework of a previously published model of language comprehension.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".