Neurocognition: Clinical and Functional Outcomes in Schizophrenia
Bibliographic record
Abstract
Schizophrenia is characterized by significant heterogeneity in outcome. The last decades have witnessed a significant interest in identifying factors that can moderate or influence clinical and functional outcomes in people with schizophrenia. One factor of particular interest is neurocognition, as performance on various measures of cognitive abilities, such as memory, attention, and executive functions, have been consistently related to functional outcome and, to a lesser extent, clinical outcome. This review aims to provide an up-to-date description of recent studies examining the association between neurocognition and clinical and (or) functional outcomes. In the first section, studies examining neurocognitive performance in relation to clinical outcome are examined. When clinical outcome is defined dichotomously (for example, comparing remitted and nonremitted), verbal memory performance consistently exhibits a strong association with clinical status, with the poor outcome group showing the largest deficits. In the second section, studies exploring the relation between neurocognition and various dimensions of functional outcome are reviewed. These dimensions include independent living, social functioning, and vocational functioning, among others. Again, a strong link between neurocognitive deficits and impairments in several aspects of functioning clearly emerges from this review. Finally, several measurement issues are discussed that pertain to the need to standardize definitions of clinical and (or) functional outcomes, the importance of defining cognitive domains consistently across studies, and distinguishing between one's competence to perform tasks and what one actually does in everyday life. Addressing these measurement issues will be key to studies examining the development of effective interventions targeting neurocognitive functions and their impact on clinical and functional outcomes.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".