T89. DEFINING COGNITIVE “NORMALITY” IN SCHIZOPHRENIA: PREVALENCE OF BROAD AND NARROW CRITERIA AND RELATION TO CLINICAL AND FUNCTIONAL STATUS
Bibliographic record
Abstract
Cognitive dysfunction is considered a core feature of schizophrenia. Nonetheless, patients with the illness overlap with healthy controls on many tasks, giving rise to the identification of subpopulations with relatively normal cognitive performance. However, the prevalence and implications of these subgroups for understanding schizophrenia are unclear because “normality” criteria vary. Estimates of the frequency of normal range performance in the patient population are as low as 0% and as high as 89%. This study examines the relation between different normality criteria and normality prevalence. It also assesses functional outcome and symptom severity in cognitively normal and impaired subgroups. “Narrow” (IQ) and “broad” (MATRICS Consensus Cognitive Battery; MCCB) cognitive normality criteria were applied to data from schizophrenia (n = 99) and healthy control samples (n = 80). Functional outcome was assessed with the Multidimensional Scale of Independent Functioning (MSIF). The Positive and Negative Syndrome Scale (PANSS) was administered to measure symptom severity. Cognitive normality ranged from 13% (broad criterion) to 47% (narrow criterion) among patients. Patients meeting both broad (MCCB) and narrow (IQ) definitions were functionally disadvantaged compared to cognitively normal controls (t(63) = 7.05, p < .01; t(72) = 9.97, p < .01, respectively). However, cognitively normal patients showed no functional (MSIF) advantage relative to cognitively impaired patients based on both broad and narrow definitions of cognitive normality (t(95) = .43, p = .67; t(74) = -1.04, p = .30, respectively). Functioning did not differ between IQ and MCCB based cognitively normal patients (t(51) = .61, p = .55). Moreover, broad and narrow definitions of cognitive normality were not associated with differences in symptom severities relative to cognitively impaired patients. This held true for both positive (t(97) = 1.39, p = .17; t(76) = -.72, p = .47, broad and narrow definitions, respectively) and negative (t(97) = .98, p = .33; t(76) = -1.07, p = .29, broad and narrow definitions, respectively) symptom severity on the PANSS. Our data show that the prevalence of cognitive performance normality varies widely with the breadth of the normality criterion. However, regardless of the criterion applied, cognitively normal patients remain functionally disadvantaged relative to cognitively normal controls. Perhaps more importantly, however defined, cognitively normal patients demonstrate no advantage in functionality relative to cognitively impaired patients. Thus, patients meeting the broad definition of cognitive normality are not functionally advantaged relative to those meeting the narrow definition. We also found that varying definitions of cognitive normality/impairment have no implications for the severity of psychotic psychopathology in treated outpatients. Overall, the current study suggests that the reported prevalence of cognitive normality in schizophrenia is largely a product of definitional approaches. At the same time, the data cast doubt on the functional importance of preserved and proficient cognition regardless of definition and suggest that cognitive normality does not confer an advantage in terms of reduced symptom severity.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".