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Record W2808995971 · doi:10.1186/s12891-018-2112-9

Global postural re-education in pediatric idiopathic scoliosis: a biomechanical modeling and analysis of curve reduction during active and assisted self-correction

2018· article· en· W2808995971 on OpenAlexafffund
Sarah Dupuis, Carole Fortin, Christiane Caouette, Isabelle Leclair, Carl‐Éric Aubin

Bibliographic record

VenueBMC Musculoskeletal Disorders · 2018
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsCegep Edouard MontpetitPolytechnique MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsScoliosisMedicineOrthodonticsCobb angleReduction (mathematics)LumbarPhysical therapyTrunkPhysical medicine and rehabilitationBiomechanicsSurgeryMathematicsAnatomy

Abstract

fetched live from OpenAlex

BACKGROUND: Global postural re-education (GPR) is a physiotherapy treatment approach for pediatric idiopathic scoliosis (IS), where the physiotherapist qualitatively assesses scoliotic curvature reduction potential (with a manual correction) and patient's ability to self-correct (self-correction). To the author's knowledge, there are no studies regarding GPR applied to IS, hence there is a need to better understand the biomechanics of GPR curve reduction postures. The objective was to biomechanically and quantitatively evaluate those two re-education corrections using a computer model combined with experimental testing. METHODS: Finite elements models of 16 patients with IS (10.5-15.4 years old, average Cobb angle of 33°) where built from surface scans and 3D radiographic reconstructions taken in normal standing and self-corrected postures. The forces applied with the therapist's hands over the trunk during manual correction were recorded and used in the FEM to simulate this posture. Self-correction was simulated by moving the thoracic and lumbar apical vertebrae from their presenting position to their self-corrected position as seen on radiographs. A stiffness index was defined for each posture as the global force required to stay in the posture divided by the thoracic curve reduction (force/Cobb angle reduction). RESULTS: The average force applied by the therapist during manual correction was 31 N and resulted in a simulated average reduction of 26% (p < 0.05), while kyphosis slightly increased and lordosis remained unchanged. The actual self-correction reduced the thoracic curve by an average of 33% (p < 0.05), while the lumbar curve remained unchanged. The thoracic kyphosis and lumbar lordosis were reduced on average by 6° and 5° (p < 0.05). Self-correction simulations correlated with actual self-correction (r = 0.9). CONCLUSIONS: This study allowed quantification of thoracic curve reducibility obtained by external forces applications as well as patient's capacity to self-correct their posture, two corrections commonly used in the GPR approach.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
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 head0.013
GPT teacher head0.305
Teacher spread0.291 · 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 designSimulation or modeling
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

Citations58
Published2018
Admission routes2
Has abstractyes

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