How do negative symptoms and social cognitive impairments overlap? Clustering analyses on patients living with schizophrenia-spectrum disorder
Bibliographic record
Abstract
Negative symptoms and social cognition (SC) are intertwined in schizophrenia spectrum disorders (SSD), but the structure of this interaction is not yet fully understood. We employed cluster analyses to advance our understanding of the relationship between negative symptom severity and SC. We sought to identify discrete groups of patients as a function of two factors of negative symptoms-Motivation and Pleasure (MAP) and Expressivity (EXP)-and two domains of SC: emotion recognition (ER) and theory of mind (ToM). We conducted two cluster analyses to determine data-driven subgroups using two independent samples of SSD participants. The first was conducted with an open dataset (n = 296) and the second with a local sample (n = 138), to assess replicability. The first cluster analysis revealed a three-cluster solution. Both analyses highlighted distinct profiles: a 'Relatively Preserved' profile; a 'Combined Impairment' profile, with high negative symptoms and impaired ER and ToM; and a 'MAP' profile, with high MAP symptoms, some EXP symptoms, and slightly to moderately impaired ER and ToM. Reducing the heterogeneity in clinical presentations of SSD patients on these dimensions of negative symptoms and SC provides relevant information that could contribute to a more effective selection of interventions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".