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Record W2013532191 · doi:10.1186/s13013-015-0037-8

Ultrasound-assisted brace casting for adolescent idiopathic scoliosis, IRSSD Best research paper 2014

2015· article· en· W2013532191 on OpenAlex
Edmond Lou, Amanda CY Chan, Andreas Donauer, Melissa Tilburn, Doug Hill

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueScoliosis · 2015
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsGlenrose Rehabilitation HospitalUniversity of Alberta
FundersGlenrose Rehabilitation Hospital FoundationGlenrose Rehabilitation HospitalAlberta InnovatesWomen and Children's Health Research InstituteChildren's Health Research Institute
KeywordsBraceMedicineScoliosisCobb angleUltrasoundIdiopathic scoliosisTorsoBracingPhysical therapyOrthodonticsSurgeryStructural engineeringEngineeringRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: Brace treatment is the most effective non-surgical treatment for AIS. High initial in-brace correction increases successful brace treatment outcomes. The objective of this study was to investigate if real-time ultrasound (US) can aid orthotists in selecting the pad pressure level and location resulting in optimal in-brace correction of the spine. METHODS: Twenty six AIS subjects participated in this pilot study with 17 (2 M, 15 F) in the control group and 9 (2 M, 7 F) in the intervention group. For the control group, the standard method was used to design their braces. In addition to the standard of care, a medical 3D ultrasound (US) system, a custom pressure measurement system and in-house software were used to select pad placement and pressure levels for the intervention group. The orthotist used a custom standing Providence brace design system to apply pressures against the patient's torso. The applied pad pressures were recorded. A real-time US spinal image was displayed. Cobb angle measurements from the baseline and the assessment scan were performed. The orthotist then decided if an adjustment was needed in terms of altering the pad locations and pressure levels. The procedures may be repeated until the orthotist attained the best simulated in-brace correction configuration to cast the brace. RESULTS: In the control group, 8 of 17 (47%) subjects needed a total of 16 brace adjustments after initial fabrication requiring a total of 33 in-brace radiographs. For the intervention group, the orthotist tried additional configurations in 7 out of 9 cases (78%). Among these 7 revised cases, 5 showed better stimulated in-brace corrections and were subsequently used to cast the brace. As a result, only 1 subject required a minor adjustment after initial fabrication. The total number of in-brace radiographs in the intervention group was 10. CONCLUSIONS: The use of the 3D ultrasound system provided a radiation-free method to determine the optimum pressure level and location to obtain the best stimulated in-brace correction during brace casting. The average number of radiographs per subject taken prior to final brace implementation with the interventional group was significantly lower than the control group.

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.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.222
GPT teacher head0.418
Teacher spread0.197 · 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