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Record W2313983823 · doi:10.1097/pep.0b013e3181f92e54

Effectiveness of Treadmill Training in Children With Motor Impairments

2010· review· en· W2313983823 on OpenAlexaff
Jill G. Zwicker, Tanja A. Mayson

Bibliographic record

VenuePediatric Physical Therapy · 2010
Typereview
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsChild and Family Research Institute
Fundersnot available
KeywordsPhysical medicine and rehabilitationTreadmillPsychologyPhysical therapyTraining (meteorology)Motor skillMedicineDevelopmental psychology

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this review was to synthesize current evidence from systematic reviews on the effectiveness of treadmill training (TT), including partial body-weight support (PBWS) TT (PBWSTT), TT only, robotic-assist PBWSTT, and mixed TT, in children with motor impairments. METHODS: Systematic literature searches were conducted in 10 databases through May 2010. Two reviewers independently selected titles, abstracts (k = 0.78), and full-text articles (k = 1.0). Of the 1166 titles retrieved, 5 studies met the inclusion criteria. Quality of included studies was assessed using AMSTAR criteria. RESULTS: Results of each systematic review were tabulated on the basis of levels of evidence, with outcomes categorized according to the International Classification of Functioning, Disability, and Health framework. Conflicting interpretations of outcomes were found between reviews, yet conclusions were similar. CONCLUSIONS: TT demonstrates encouraging results, but more rigorous research is needed before clinicians can be confident of its effectiveness and clinical guidelines can be developed.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.005
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.325
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations55
Published2010
Admission routes1
Has abstractyes

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