Relationship Between Mmn And Real-Life Functioning In Subjects With Schizophrenia
Bibliographic record
Abstract
Introduction. Despite the development of successful treatments of psychotic symptoms, the impairment in real-life functioning in subjects with schizophrenia (SCZs) remains an unmet need in their management. Recent studies suggested that functioning of SCZs was associated with impairment in mismatch negativity (MMN), an event-related potential reflecting pre-attentive processing. However, these studies did not clarify whether this relationship is a direct one or reflects a cross-correlation with other variables. Objectives. Our study was designed to investigate differences between SCZs and healthy controls (HCs) on MMN amplitude and its relationships with real-life functioning domains in SCZs.Methods. In the context of a multicenter study of the Italian Network for Research on Psychoses, pitch- (p-MMN) and duration-deviant (d-MMN) MMNs were recorded in 125 chronic SCZs and 61 HCs. Within SCZs, we assessed psychopathology, neurocognitive functions; functioning was measured with the Specific Level of Functioning Scale (SLOF). Multiple regression was used to predict functioning using MMN, age, gender, duration of illness, neurocognitive composite score of the MATRICS Consensus Cognitive Battery, Calgary Depression Scale for Schizophrenia total score, negative symptom domains of the Brief Negative Symptom Scale, positive and disorganization dimensions of the Positive and Negative Syndrome Scale (PANSS) as independent variables.Results. SCZs showed a reduced p-MMN and d-MMN amplitudes, compared with HCs.PANSS Positive dimension (u03b2=-.421, p<.001) and p-MMN amplitude (u03b2=-.219, p<.011) were found to predict SLOF work skills domain of SCZs, independently from symptoms, demographic characteristics and neurocognition. Conclusions. The impairment in the pre-attentive processing in SCZs might represent a candidate biomarker of poor functional outcome.
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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".