Using meta-data to explore dose-response relationships in stroke therapy
Bibliographic record
Abstract
Neurophysiological data suggest that a very high number of movement repetitions are required to induce the neuroplastic changes that lead to behavioural improvements in therapy. The volume of repetitions can be thought of as the therapy dosage. Little is known about the relationship between dosage and magnitude of recovery. We used meta-analytic regression to explore dose-response relationships in physical therapy for adults with stroke. We conducted a systematic review of randomized controlled trials (databases: PubMed, PsychINFO, Google Scholar). 28 RCTs were identified that manipulated time in therapy between treatment and control groups. Meta-regression was used to predict standardized mean differences between treatment and control groups based on additional therapy time and time from stroke onset to treatment. Additional therapy time led to improved treatment outcomes, but this effect was moderated by time post-stroke. In early stages of stroke (0-3 mo.) this effect was attenuated (β = 0.005, se = 0.02). For moderate time post-stroke (3-12 mo.) the strength of this effect increased (β = 0.055, se = 0.02). The effect also increased for longer times post-stroke (>12 mo.; β = 0.039, se = 0.02), but longer times post stroke also had a significant negative effect (β = -0.254, se = 0.18) on the intercept (β = 0.346, se = 0.10). All times post-stroke benefited from additional time in therapy, but additional time had a larger effect after 3 months post-stroke. Theoretical implications of these data are discussed as are important suggestions for future research.
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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 | 0.057 | 0.132 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.073 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".