Prediction of cognitive status and 5‐year survival for elderly with diabetes by using the Neurocognitive Frailty Index (NFI)
Bibliographic record
Abstract
Abstract Background Type 2 diabetes mellitus has been shown to be associated to cognitive decline and an increased risk of mild cognitive impairment and dementia in elderly persons (Biessels et al., 2014). The aim of this research was to determine whether the Neurocognitive Frailty Index (NFI, Pakzad et al., 2017) could help to predict cognitive changes in elderly people with diabetes. Method Participants in this study were drawn from waves 1 (1991 to 1992) and 2 (1996 to 1997) of the Canadian Study of Health and Aging (CSHA) for prediction of 5‐year cognitive changes in those who received a consensus diagnosis of no cognitive impairment (NCI) or cognitive impairment but not dementia (CIND) in wave 1 (n = 997). The CIND category was contained to individuals whose level of cognitive impairment was measured to be greater than the NCI group but less than the dementia group. NFI was defined as a combined score of 41 physical and mental components (in 8 cognitive domains) as they were available in the dataset (NFI has 42 components but we withdraw diabetes). Cognitive score (measured by 3MS) at follow‐up, and mortality were outcome. Result Mean NFI and standard deviation was 9.7±6.22 in people without diabetes, 11.71±5.42 in people with diabetes and 9.36±5.30 in people with diabetes but are using treatment. Multiple linear regression indicated that NFI score is significantly associated to cognitive status (3MS at follow‐up, p<0.05), however, diabetes did not show any significant association to predict this outcome. CHAID decision tree analysis stratified NFI scores into five categories, while proportional hazards analysis adjusted for age, gender and education indicated that NFI was more correlated to survival than age and had a dose‐response effect in relation to survival. Those with diabetes had a higher probability of death compared to those without (p<0.05). Conclusion NFI was significantly associated with cognitive changes for the elderly in those with and without diabetes. Given that the NFI is proving to be a valuable tool to assess the risk of developing dementia, further investigation is needed to clarify which modifiable risk factors may help reduce the risk of cognitive decline.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".