Prediction and mediation analysis for treatment responses to combined cognitive and physical training for older adults
Bibliographic record
Abstract
Diminished cognitive and physical functions negatively affect the daily functions of individuals. Although combined cognitive and physical training prevents instrumental activities of daily living (IADL) disability in older adults, no predictive model or mediation analysis of IADL after combined training exists. This study aims to employ prediction and mediation analysis to identify the predictors of IADL performance and to elucidate the mediators of the association between baseline global cognition and subsequent IADL performance following combined cognitive and physical training. This study involved 177 participants aged 60 years and older who underwent combined training. Cognitive function was measured with the Montreal Cognitive Assessment (MoCA), Digit Symbol Substitution Test (DSST), Color Trails Test, Word List, and a dual task; physical function with the Timed Up and Go (TUG) test; daily function with the Lawton IADL Scale. We conducted regression analyses to identify the predictors of IADL performance, and mediation analysis to examine whether DSST and TUG mediate the relationship between MoCA and IADL. The pre-intervention DSST and TUG were significant independent predictors of post-intervention IADL. The association between the pre-intervention MoCA and post-intervention IADL was mediated by pre-intervention DSST and TUG. This study highlighted the importance of measuring and improving processing speed and functional mobility before and during interventions to enhance IADL outcomes.Trial registration: NCT03619577, 23/07/2018; NCT04689776, 29/12/2020.
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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 | 0.030 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".