Dose and timing in neurorehabilitation
Bibliographic record
Abstract
PURPOSE OF REVIEW: Prescribing the most appropriate dose of motor therapy for individual patients is a challenge because minimal data are available and a large number of factors are unknown. This review explores the concept of dose and reviews the most recent findings in the field of neurorehabilitation, with a focus on relearning motor skills after stroke. RECENT FINDINGS: Appropriate dosing involves the prescription of a specific amount of an active ingredient, at a specific frequency and duration. Dosing parameters, particularly amount, are not well defined or quantified in most studies. Compiling data across studies indicates a positive, moderate dose-response relationship, indicating that more movement practice results in better outcomes. This relationship is confounded by time after stroke, however, wherein longer durations of scheduled therapy may not be beneficial in the first few hours, days, and/or weeks. SUMMARY: These findings suggest that substantially more movement practice may be necessary to achieve better outcomes for people living with the disabling consequences of stroke. Preclinical investigations are needed to elucidate many of the unknowns and allow for a more biologically driven rehabilitation prescription process. Likewise, clinical investigations are needed to determine the dose-response relationships and examine the potential dose-timing interaction in humans.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".