The Past, Present, and Future of Neurorehabilitation: From NUSTEP Through IV STEP and Beyond
Bibliographic record
Abstract
PURPOSES: To present the history and aims of the STEP conferences; describe the interdependence of prevention, prediction, plasticity, and participation; reflect on where we stand today regarding those 4 Ps; and discuss how future neurorehabilitation should look for individuals with movement disorders. KEY POINTS: Physical therapists have focused primarily on tertiary prevention, emphasizing primary/secondary prevention far less. Predicting optimal response to intervention is essential for primary prevention. Research examining neurorehabilitation effects mediated by brain plasticity is evolving from an emphasis on impairment outcomes toward examination of participation outcomes. CLINICAL PRACTICE RECOMMENDATIONS:: (1) Capitalize on primary and secondary prevention. (2) Administer simple, environmentally relevant predictive measures. (3) Partner with researchers to examine exercise-induced brain plasticity effects via neuroimaging. (4) Encourage physical activity to promote secondary prevention of lifestyle-related diseases and enhance participation. (5) Integrate psychological/social sciences with physiological sciences to move forward with advances in mindful health and patient-centered practices.
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 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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".