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Record W2625915250 · doi:10.1097/npt.0000000000000193

The Past, Present, and Future of Neurorehabilitation: From NUSTEP Through IV STEP and Beyond

2017· article· en· W2625915250 on OpenAlexaff
Susan R. Harris, Carolee J. Winstein

Bibliographic record

VenueJournal of Neurologic Physical Therapy · 2017
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNeurorehabilitationIntervention (counseling)PsychologyMedicinePhysical medicine and rehabilitationRehabilitationNeurosciencePsychiatry

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.305
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2017
Admission routes1
Has abstractyes

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