Prevalence, incidence, and clinical impact of cognitive–motoric risk syndrome in Europe, USA, and Japan: facts and numbers update 2019
Bibliographic record
Abstract
A new syndrome called the 'motoric-cognitive risk' (MCR) syndrome has recently been proposed in older persons. According to this definition, the parallel impairment in muscle and brain function is more predictive for identifying subjects at risk of dementia than impairment a in single system alone. Epidemiological studies suggest that among older persons, enrolled in worldwide population-based studies, 10% are affected by this syndrome, which confers a higher risk of future disability. In detail, the prevalence of MCR in Europe is around 8.0%, 7.0% in the United States, and 6.3% in Japan. The incidence of the MCR syndrome is estimated to be 65.2 per 1000 person years in adults aged 60 years or older. Many studies reported negative outcomes of the syndrome in older persons, emphasizing its clinical impact. In particular, in almost all longitudinal studies, MCR produces a three-time increased risk of future dementia. In Europe, data from the InCHIANTI study report an increased risk of 2.74 [1.54-4.86], which is 2.49 [1.52-4.10] in the United States and 3.27 [1.55-6.90] in Japan. The studies in different continents are also consistent in showing an increased risk of all-cause mortality, which is 1.50-1.87 in the Europeans and 1.69 [1.08-2.02] for incident disability in Japan. For the identification of the MCR syndrome, different tests and procedures have been proposed, with a final 'core-battery' that includes gait speed, dual-task gait speed, the Montreal Cognitive Assessment and Trail Making Test A and B. The criteria used to select this core-battery were based on the best accuracy for identifying older persons at risk of negative outcomes such as dementia, falls, aging-related disabilities, and sensitivity to interventions. The selection of these tests will allow to start studies aimed to better capture older persons at higher risk of mobility and cognitive disability. By these tests, it will be possible to better evaluate the effect of treatment composing of tailored physical exercise, nutritional suggestions, and medical therapy to overturn negative effect of both cognitive and motoric frailty. This article provides an overview of the current knowledge of the MCR syndrome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".