Deficits in rate of force production during multifinger tasks are associated with cognitive status
Bibliographic record
Abstract
OBJECTIVES: The multifinger force deficit (MFFD) is the decline in force generated by an individual finger as the number of fingers contributing to the action is increased. It has been proposed that as a measure of neural sufficiency rather than muscle status, it provides a means of detecting individuals at risk of cognitive decline. Age-related deficits in central neural drive exert a disproportionate impact on the rate at which force can be generated. We examined whether a MFFD derived from the maximum rate at which force is generated, is more sensitive to individual differences in cognitive status, than one calculated using the maximum level of force. METHODS: Monotonic associations between each of two variants of the MFFD, and cognition (measured with the Montreal Cognitive Assessment), were estimated cross sectionally using generalized partial rank correlations, in which age, level of education and degree of handedness were included as covariates. The participants (n=26) were community dwelling adults aged 66-87. RESULTS: The MFFD derived using the maximum rate of force development was negatively associated with cognitive status. The association for the MFFD based on the maximum level of force, was not statistically reliable. The associations with cognitive status obtained for both variants of the MFFD were of greater magnitude than those reported previously for standard grip strength dynamometry. CONCLUSION: The sensitivity with which the MFFD detects risk of cognitive decline may be enhanced by using the maximum rate of force developed by each finger, rather than the maximum force generated by each finger.
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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 | 0.001 | 0.008 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".