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Record W2117695990 · doi:10.3233/rnn-120264

What are the “ingredients” of modified constraint-induced therapy? An evidence-based review, recipe, and recommendations

2013· review· en· W2117695990 on OpenAlex
Stephen J. Page, Shaun G. Boe, Peter Levine

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRestorative Neurology and Neuroscience · 2013
Typereview
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsDalhousie University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Center for Complementary and Integrative Health
KeywordsConstraint (computer-aided design)Constraint-induced movement therapyRecipeMedicineClinical PracticePsychotherapistComputer sciencePsychologyManagement sciencePhysical medicine and rehabilitationPhysical therapyEngineering

Abstract

fetched live from OpenAlex

Modified constraint induced movement therapy (mCIT) increases paretic upper extremity use and movement in all phases of stroke. Although fundamental to its appropriate implementation, specific details on day to day implementation on this promising family of therapies have not heretofore been published. Consequently, some integral behavioral facets of mCIT may be overlooked, while other approaches may be easily mistaken to constitute mCIT, during attempts to implement the therapy. The purpose of this paper is to review mCIT, and to provide the clinician-reader with a detailed description of the "ingredients" of mCIT and their rationale, including clinical examples of these components. It is expected that a more complete understanding of the components comprising this promising approach will overcome knowledge barriers associated with its appropriate use, and encourage better patient management in clinical practice.

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.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.219
GPT teacher head0.414
Teacher spread0.195 · 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