P2‐490: VALIDATION OF NEUROCOGNITIVE FRAILTY INDEX (NFI) IN PEOPLE WITH HEART DISEASE
Bibliographic record
Abstract
Comprehensive assessment of general health (physical and mental/cognitive) can increase late life disease prediction. The aim of this research was to understand how NFI could help to predict cognitive changes in elderly people with heart disease. The participants represent individuals drawn from the Canadian Study of Health and Aging (CSHA). The CSHA was conducted in three waves: CSHA-1 (1991 to 1992) and CSHA-2 (1996 to 1997). In this study we just used data from wave 1 and 2 for prediction of 5-year cognitive changes. Samples included those who completed neuropsychological tests at CSHA-1 and received a clinical diagnostic assessment at CSHA-1 and CSHA-2 (n = 1228). The current analysis focused on the 997 participants who received a consensus diagnosis of No cognitive impairment (NCI) or cognitive impairment but not dementia (CIND) on CSHA-1. The CIND category was contained of individuals whose level of cognitive impairment was evaluated to be greater than the NCI group but less than the Dementia group. NFI (Neurocognitive Frailty Index) was defined as a combined score of 42 physical and mental components (in 8 cognitive domains) as they were available in the dataset. Cognitive score (measure by 3MS) at follow-up was outcome. At baseline, the mean age of sample was 80.4 year (SD=6.9; Range of 66-100). The NFI mean was 9.93 (SD=6.15) and ranged from 0 to 33). In the multiple linear regression analysis, adjusted for age, gender and 3MS at baseline, the NFI was correlated to 3MS at follow-up same as age (p<0.05). Every additional deficit used to calculate the NFI was associated with an increased chance of cognitive decline. However this Association was stronger in people with heart disease. NFI was significantly associated with cognitive changes for the people with heart disease and this association was stronger than age (p<0.05).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".