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Record W2156214100 · doi:10.1186/1748-7161-5-s1-o63

Immediate correction required to expect a long-term effectiveness of a brace treatment: a biomechanical insight

2010· article· en· W2156214100 on OpenAlexaff
Julien Clin, Carl‐Éric Aubin, Hubert Labelle, Stefan Parent

Bibliographic record

VenueScoliosis · 2010
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsBraceMedicineCoronal planeOrthodonticsTerm (time)Cobb angleFinite element methodBiomechanicsParametric statisticsTrunkRadiographyStructural engineeringSurgeryAnatomyEngineeringMathematics

Abstract

fetched live from OpenAlex

Immediate in-brace correction has often been deemed as fundamental to long-term brace effectiveness but the biomechanical rational is unclear and unproven. To biomechanically study how the immediate in-brace correction of the scoliotic curves is affecting the mechanisms involved in the long term correction of the spine. The three-dimensional geometry of 30 patients was acquired using multi-view radiographic reconstruction and surface topography techniques. A finite element model of the trunk and a parametric brace model were created. For each case, two spinal stiffnesses (flexible, stiff) were tested. Installation of the brace was simulated. Using an experimental design framework including thirteen design factors, 768 braces were tested for each patient (total of 69120 tested braces). Immediate in-brace correction of the coronal Cobb angles and loads acting on the growth plates of the apical vertebrae were computed and analyzed. Immediate correction of coronal curves and corresponding bending loads on the apical vertebrae were linearly correlated (mean R2 = 0.86). 10% to 99% of immediate correction was necessary to nullify the asymmetric loads, with an average of 49% (flexible spine model) and 35% (stiff spine model).

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.520

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)0.0000.000

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 head0.322
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2010
Admission routes1
Has abstractyes

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