Correlation between age and MMSE in schizophrenia
Bibliographic record
Abstract
BACKGROUND: The Mini-Mental State Examination (MMSE) is widely used in schizophrenia, although normative data are lacking in this population. This review and meta-regression analysis studies the effect of aging on MMSE scores in schizophrenic patients. METHODS: We entered the search terms schizophrenia and MMSE in PubMed and PsychInfo. Bibliographies of pertinent articles were also examined. We included every study presenting the MMSE scores in schizophrenic patients along with a corresponding mean age. We conducted our analyses using simple linear regression weighted for the inverse of within-trial variance of the age variable, thus conferring more importance to studies with narrower age groups. RESULTS: We identified 56 articles (n = 5,588) published between 1990 and 2012. The MMSE scores of schizophrenic patients decline by approximately 1 point for every four years (y = 34.939-0.247x, 95% Confidence Interval (CI) [-0.304, -0.189], R 2 = 0,545), which is five times the rate in the general population. Institutionalized patients account for a large proportion of this decline (y = 37.603-0.308x, 95% CI [-0.349, -0.267], R 2 = 0.622) whereas community-dwelling patients are relatively stable throughout aging (y = 27.591-0.026x, 95% CI [-0.074, 0.023], R 2 = 0.037). CONCLUSIONS: Subgroup analyses show different trajectories between institutionalized and outpatients with schizophrenia. The deterioration observed in institutionalized patients may have to do with greater illness severity, heavier medication load, vascular risk factors, and lack of stimulation in institutional settings. Studies documenting the role of these variables would be useful. Cognitive screening tools that assess executive functions would be interesting to study in schizophrenics, as they may reveal more subtle age-related cognitive changes not measured by the MMSE.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".