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Record W2466559356 · doi:10.3233/978-1-60750-935-6-378

Is The Boston Brace Mechanically Effective in AIS?

2002· article· en· W2466559356 on OpenAlexaff
V.J. Raso, Edmond Lou, Douglas L. Hill, James Mahood, Marc Moreau

Bibliographic record

VenueStudies in health technology and informatics · 2002
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsCapital District Health Authority
Fundersnot available
KeywordsBraceBracingScoliosisIdiopathic scoliosisMedicinePhysical therapyPhysical medicine and rehabilitationPoint (geometry)Structural engineeringOrthodonticsEngineeringSurgeryMathematics

Abstract

fetched live from OpenAlex

The application of three-point loading is thought to be the essential basis for effective bracing of adolescent idiopathic scoliosis. Care is taken to ensure that active pressure pad is located to provide maximum support to the apex of the scoliosis while minimizing its lordosising effect. Paradoxically, while cited as an essential factor in the design of braces, there is no consensus as to the importance of such loading to the clinical effectiveness of braces. It may be that braces are effective but that they are effective for reasons unrelated to mechanics. There are few studies that link brace mechanics and change in spinal alignment. Optimal bracing for AIS requires a much better understanding of the role of the mechanical support of braces used to treat AIS. Sixteen subjects, 3 males and 13 females, were participated to this study to determine the correlation between quantity and quality of brace wear and treatment outcomes in AIS. This study showed that the target force levels set for the active pad in braces prescribed for the treatment of AIS vary considerably and that brace applies the desired load 25% of the prescribed time.

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)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.060
GPT teacher head0.386
Teacher spread0.326 · 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 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

Citations5
Published2002
Admission routes1
Has abstractyes

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