P3‐074: Frailty as a risk for the development and progression of cognitive impairment in older adults: Results of a dynamic model
Bibliographic record
Abstract
With age, people accumulate a variety of deficits, including physical and cognitive ones. The prevalence of cognitive impairments dramatically increases with age, as does the prevalence of frailty. Here we considered how cognitive deficits, operationalized as a continuum of cognitive test score errors, accumulate in relation to frailty, operationalized across a continuum of non-cognitive (physical, functional) deficits, in relation to age, sex and education. Five-year changes in cognition (defined as the errors on the Modified Mini-Mental State Examination) were analyzed in relation to general health status (defined by the Frailty Index) in older Canadians (n = 8,403). A Markov chain was used to model the probabilities of changes in cognitive test scores. A generalized linear model and logistic regression were used to estimate change in cognition and mortality risks, respectively. Baseline cognition, age, frailty, sex and education were covariates. Age and frailty were considered both as continuous variables and dichotomized at their median values. Age and frailty were each consistently associated both with cognitive changes and with the risk of death. Education main effects were significantly associated with cognitive transitions, but not with mortality. Sex was associated only with mortality. Frail people less often showed cognitive improvement or stabilization (22.3%, 95% CI = 20.1%-24.5%) compared with non-frail people, of whom 40.9% (95% = 39.7%-42.1%) did not deteriorate. Similarly, frail people were more likely to die (47.4%, 95%CI = 44.8%-50%) versus 22.3% (95% CI = 20.1%-24.5%) of those not frail. Although education did not influence mortality, people with higher education had a greater chance of cognitive stabilization or improvement: among more educated people 39.9% improved or remained stable (95% CI = 38.4%-41.4%) than did less educated people (33.2%, 95%CI = 31.7%-34.7%). Frailty was a risk for cognitive decline. In contrast to other approaches, our model makes it possible to analyze not only regression (average effects) but calculates the likelihood of changes in all direction including improvement. To now, cognitive impairment is usually considered as measurement error; our data suggest that it is real and predictable. It appears that the ability to fight back is an intrinsic property, even in people affected by at least the early stages of ‘irreversible’ illnesses such as 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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".