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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".