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Record W3186088568 · doi:10.14288/cjur.v6i1.192454

effectiveness of orthosis as a treatment for adolescent idiopathic scoliosis

2020· article· en· W3186088568 on OpenAlexaff
Li Yao Pan, ChenChao Zhang

Bibliographic record

VenueOpen Collections · 2020
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsBraceScoliosisMedicineIdiopathic scoliosisSpinal deformityDeformityPhysical medicine and rehabilitationPhysical therapyBracingSurgery

Abstract

fetched live from OpenAlex

Orthosis is a non-invasive method of treatment for patients with scoliosis, in which prescribed individuals will undergo correction of their spinal misalignment with the support of a brace. The main mechanisms involved in orthosis are the internal and external forces applied by the brace to help prevent further deterioration of the spinal cord. Specifically for Adolescent Idiopathic Scoliosis (AIS), occuring in youth during the critical ages of bone development, it is essential to improve the spine’s condition as much as possible before the body fully matures. Although orthosis does not aim to correct the misalignment, it has been proven that bracing will aid in slowing down the progression of deformity. However, a common concern is that the spinal curve will regress back to its original state upon removal of the brace at the end of the treatment period. After reviewing both short-term and long-term cases of various patients who have used a brace, there is evidence demonstrating that this is not true as studies have shown more benefits to using orthosis than drawbacks, making it an effective intervention for scoliosis. When comparing those who undergo orthosis treatment and those who do not, a significant decrease in the spine’s progression was found.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.065
GPT teacher head0.346
Teacher spread0.282 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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