Exploring the functional utility of the Advanced Clinical Solutions-Social Perception Affect Naming subtest in treatment-resistant psychosis
Bibliographic record
Abstract
INTRODUCTION: Despite the recognized importance of social cognition in predicting functional outcomes in schizophrenia, there is a lack of widely accepted measures that assess this broad domain while possessing psychometric validity and predictive utility. This study aimed to address this gap by providing incremental validity data for a promising social cognitive measure assessing facial affect recognition in patients presenting with treatment-resistant psychosis. METHOD: Using a clinical archival dataset comprising 59 consecutive admissions to an inpatient treatment-resistant psychosis unit, this study examined facial affect naming performance from the Advanced Clinical Solutions-Social Perception (ACS-SP) affect naming subtest, and the association with neuropsychological functioning and symptom severity. Hierarchical regression models were used to assess whether facial affect recognition predicted daily functioning, including measures of functional capacity and functional performance. RESULTS: The ACS-SP affect naming measure showed limited sensitivity for impairment relative to other cognitive domains. Affect naming showed weak to moderate correlations with a broad range of non-memory cognitive functions, and no association with symptom severity. After controlling for cognitive functioning and symptoms, the ACS-SP affect naming task predicted poorer functioning with regard to functional performance but not functional capacity. CONCLUSIONS: The ACS-SP affect naming task associates weakly to moderately with other measures of cognition, but also likely taps into social cognitive skills not measured by typical neuropsychological tests. This measure was predictive of some aspects of functional outcomes in patients with treatment-resistant psychosis, and therefore may be a useful tool to incorporate into routine neuropsychological assessments in such treatment settings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".