The Impact of Aging, Cognition, and Symptoms on Functional Competence in Individuals With Schizophrenia Across the Lifespan
Bibliographic record
Abstract
OBJECTIVE: Life expectancy in individuals with schizophrenia continues to increase. It is not clear whether cognitive deficits associated with schizophrenia remain as strong predictors of function in older and younger individuals. Thus, we assessed the relationship between cognition and functional competence in individuals with schizophrenia across 7 decades of life. METHODS: We analyzed data obtained in 232 community-dwelling participants with schizophrenia (age range: 19-79 years). Cognition was assessed using the Measurement and Treatment Research to Improve Cognition in Schizophrenia Consensus Cognitive Battery. Functional competence was assessed using the UCSD Performance-based Skills Assessment, which includes measures of Comprehension and Planning of Recreational Activities Skills, Financial Skills, Communication Skills, Transportation Skills, and Household Management Skills. To assess the effects of Global Cognition on functional competence, we performed hierarchical multivariate linear or logistic regression analyses controlling for age, education, gender, and negative symptoms. RESULTS: Participants' mean age was 49.1 (SD = 13.2, range = 19-79 years), 161 (69%) were male, and 55 (24%) were aged ≥60. Global Cognition was a predictor of Comprehension and Planning Skills (Exp(β) = 1.048), Financial Skills (Exp(β) = 1.104), Communication Skills (ΔR (2) = .31) and Transportation Skills (Exp(β) = 1.066), but not Household Management Skills after adjusting for age, education, gender, and negative symptoms of schizophrenia. CONCLUSION: Cognition remains a strong predictor of functional competence across the lifespan. These findings suggest that treating cognitive impairment associated with schizophrenia could improve individuals' function independent of their age.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".