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Record W2127767348 · doi:10.3138/ptc.2012-29

An Intensive Programme of Passive Stretch and Motor Training to Manage Severe Knee Contractures after Traumatic Brain Injury: A Case Report

2013· article· en· W2127767348 on OpenAlexvenueno aff
Joan Leung, Lisa A. Harvey, Anne M. Moseley

Bibliographic record

VenuePhysiotherapy Canada · 2013
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsnot available
Fundersnot available
KeywordsTraumatic brain injuryPhysical medicine and rehabilitationMuscle contractureMedicinePhysical therapyAcquired brain injuryRehabilitationSurgery

Abstract

fetched live from OpenAlex

PURPOSE: While contemporary management of contractures (a common secondary problem of acquired brain injury that can be difficult to treat) includes passive stretch, recent evidence indicates that this intervention may not be effective. This may be because clinical trials have not provided a sufficient dose or have not combined passive stretch with other treatments. The purpose of this case report is to describe a programme of intensive passive stretch combined with motor training administered over a 1.5-year period to treat severe knee contractures. METHOD: Five months after traumatic brain injury, an adolescent client with severe contractures in multiple joints underwent an intensive stretch programme for his knee contractures, including serial casting and splinting, which was administered for 10 months in conjunction with a motor training programme administered for 1.5 years. RESULTS: The client regained full extension range in his knees and progressed from being totally dependent to walking short distances with assistance; these effects were maintained at follow-up 5.5 years after injury. CONCLUSION: The use of a high dose of passive stretch in conjunction with motor training may be an option to consider for correcting severe contractures following acquired brain injury.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.916
Threshold uncertainty score0.792

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)0.0010.000

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 head0.012
GPT teacher head0.294
Teacher spread0.283 · 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 designOther design
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

Citations7
Published2013
Admission routes1
Has abstractyes

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