Critically appraised paper: Implicit motor learning is not superior to explicit motor learning for improving gait speed in chronic stroke [commentary]
Bibliographic record
Abstract
Question: Does implicit motor learning using analogy learning improve walking speed in chronic stroke more than explicit motor learning using verbal instructions?Design: Randomised controlled trial with concealed allocation and blinded outcome assessment.Setting: Home-based intervention in the Netherlands.Participants: Individuals who were 6 months after stroke, with gait speed , 1.0 m/s and ability to follow a three-step command in Dutch.Key exclusion criteria were inability to walk 10 m, requiring manual assistance to walk on level surfaces, and other non-stroke impairments that affected the gait pattern.Randomisation of 81 participants allocated 39 to the implicit training group and 42 to the explicit training group.Interventions: For both groups, the intervention was delivered at home in nine 30-minute sessions over 3 weeks.The implicit training group participants received analogies meaningful to them that aimed to improve walking performance (eg, walk as if you are following footprints in the sand), whereas the explicit training group received detailed verbal instructions about how to alter aspects of their walking (eg, land with your heel first then roll through from heel to toe).Outcome measures: The primary outcome measure was 10-m gait speed, averaged over three trials, measured before and after intervention and at 1-month follow-up.Secondary outcome
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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 | 0.019 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.010 | 0.002 |
| Research integrity | 0.069 | 0.041 |
| Insufficient payload (model declined to judge) | 0.028 | 0.016 |
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".