Bibliographic record
Abstract
OBJECTIVES: 1. To determine if Self-Rated Health (SRH) predicts dementia over a five period in cognitively intact older adults, and in older adults with Cognitive Impairment, No Dementia (CIND); and 2. To determine if different methods of eliciting SRH (age-referenced (AR) versus unreferenced) yield similar results. DESIGN: Prospective cohort. POPULATION: 1468 cognitively intact adults and 94 older adults with CIND aged 65+ living in the community, followed over five years. MEASURES: Age, gender, education, subjective memory loss, depressive symptoms, functional status, cognition, SRH and AR-SRH were all measured; dementia was diagnosed on clinical examination. Those with abnormal cognition not meeting criteria for dementia were diagnosed with CIND. RESULTS: In those who were cognitively intact at time 1, and had good SRH: 69.4% were intact; 6.0% had CIND; 6.9% had dementia, and 17.7% had died at time 2, while in those with poor SRH: 44.9% were intact, 11.1% had CIND, 9.1% had dementia, and 34.8% had died (p<0.001, chi-square test). In multinomial regression models SRH predicted dementia and death. In those with CIND at time 1 and good SRH: 2.3% were intact: 18.6% had CIND; 34.9% had dementia and 44.2% had died at time 2, while in those with poor SRH: 4.8% were intact, 31.0% had CIND, 19.0% had dementia, and 43.6% had died (p=0.30, chi-square test). In multinomial regression models, this was not significant. AR-SRH analyses were similar. CONCLUSIONS: In cognitively intact older adults SRH predicts dementia. In older adults with CIND, SRH does not predict dementia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".