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Record W4221028426 · doi:10.3233/prm-220002

Evidence-informed clinical perspectives on postural management for hip health in children and adults with non-ambulant cerebral palsy

2022· article· en· W4221028426 on OpenAlexaff

Bibliographic record

VenueJournal of Pediatric Rehabilitation Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCerebral palsySittingSupine positionGross Motor Function Classification SystemLyingBody positionClinical Practice

Abstract

fetched live from OpenAlex

Postural management is a multi-disciplinary approach incorporating a comprehensive schedule of daily and night-time positions, equipment and physical activity to help maintain or improve body structures and function and increase activity and participation. Postural management may play a role in preventing contracture, deformity, pain, and asymmetry. This article provides an overview of the evidence supporting use of postural management to positively influence hip health in individuals with cerebral palsy, functioning as Gross Motor Classification System (GMFCS) levels IV or V. Sitting or lying without changing position for more than 8 hours, unsupported supine lying and asymmetrical or windswept postures are associated with pain and hip subluxation/dislocation. Although high-quality experimental research is still limited by many factors, there is limited evidence of harm, and most individuals at GMFCS IV or V require positioning supports to enable participation and function and ease caregiving. Clinical recommendations combining research and clinical opinion support the early use of comfortable positioning routines and/or equipment to reduce time spent in sustained asymmetrical or potentially harmful sitting and lying positions. Supported standing, active weightbearing and stepping are recommended to promote active movement and position change when possible, depending on individual, family and caregiver routines and preferences.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.022
GPT teacher head0.349
Teacher spread0.328 · 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

Citations19
Published2022
Admission routes1
Has abstractyes

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