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Record W2188569126 · doi:10.3389/fnagi.2015.00214

Rapid Responsiveness to Practice Predicts Longer-Term Retention of Upper Extremity Motor Skill in Non-Demented Older Adults

2015· article· en· W2188569126 on OpenAlexaboutno aff
Sydney Y. Schaefer, Kevin Duff

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2015
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
FundersNational Institute on AgingNational Institutes of HealthUtah State University
KeywordsPhysical medicine and rehabilitationMotor skillPsychologyTerm (time)Developmental psychologyMotor learningMedicineAudiologyGerontologyPhysical therapyNeuroscience

Abstract

fetched live from OpenAlex

Skill acquisition is a form of motor learning that may provide key insights into the aging brain. Although previous work suggests that older adults learn novel motor tasks slower and to a lesser extent than younger adults, we have recently demonstrated no significant effect of chronological age on the rates and amounts of skill acquisition, nor on its long-term retention, in adults over the age of 65. To better understand predictors of skill acquisition in non-demented older adults, we now explore the relationship between early improvements in motor performance due to practice (i.e., rapid responsiveness) and longer-term retention of an upper extremity motor skill, and whether the extent of rapid responsiveness was associated with global cognitive status. Results showed significant improvements in motor performance within the first five (of 150) trials, and that this "rapid responsiveness" was predictive of skill retention 1 month later. Notably, the extent of rapid responsiveness was not dependent on global cognitive status, as measured by the Montreal Cognitive Assessment (MoCA). Thus, rapid responsiveness appears to be an important variable in longer-term neurorehabilitative efforts with older adults, regardless of their cognitive status.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.289
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2015
Admission routes1
Has abstractyes

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