Community Outcome in Cognitively Normal Schizophrenia Patients
Bibliographic record
Abstract
Recent reports suggest that cognition is relatively preserved in some schizophrenia patients. However, little is known about the functional advantage these patients may demonstrate. The purpose of this study was to identify cognitively normal patients with a recently developed test battery and to determine the functional benefit of this normality relative to cognitively impaired patients. Average-range cognitive ability was defined by the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery (MCCB) composite score (T≥40) and applied to 100 patients with schizophrenia or schizoaffective disorder and to 81 non-psychiatric research participants. With group assignment adjusted for demographic variables, this procedure yielded 14 cognitively normal patients, 21 cognitively impaired patients, and 21 healthy adults with normal-range MCCB scores. Cognitively normal patients were indistinguishable from controls across all MCCB scales. Furthermore, their performance was superior to impaired patients on all scales except Social Cognition. Cognitively normal patients were also superior to impaired patients on a summary index of simulated life skills and functional competence. Nevertheless, both patient groups were equally disadvantaged relative to controls in independent community living. These findings suggest that normal-range cognition exists in schizophrenia, but fails to translate into enhanced community 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".