The association between late-life cognitive test scores and retrospective informant interview data
Bibliographic record
Abstract
BACKGROUND: Cognitive assessment of older persons, particularly those with impairment, is hampered by measurement error and the ethical issues of testing people with dementia. A potential source of valuable information about end-of-life cognitive status can be gained from those who knew the respondent well - mostly relatives or friends. This study tested the association between last cognitive assessment before death and a retrospective informant assessment of cognition. METHODS: Data were analyzed from 248 participants from the Medical Research Council Cognitive Function and Ageing Study who were aged 71 to 102 years at death. Late-life cognition was assessed 0 to 8 years before death using the Mini-mental State Examination (MMSE) and the informant measure was taken 0 to 7 years after death using a Retrospective Informant Interview (RInI). RESULTS: Zero-inflated Poisson regression showed a strong association between MMSE scores and RInI scores - those scoring 29-30 on the MMSE had a RInI score four times lower than those who scored <18 (p < 0.001). The time between MMSE and death was also a significant predictor with each additional year increasing RInI scores by 12.4% (p < 0.001). The time between death and RInI was only a significant predictor when including measures that were taken four years or more after death. CONCLUSIONS: Cognitive scores from retrospective informant interviews are strongly associated with late-life MMSE scores taken close to death. This suggests that the RInI can be used as a proxy measure of cognition in the period leading up to death.
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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 | 0.017 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".