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Record W2151255424 · doi:10.1109/tnsre.2012.2192483

Development of a Pressure Control System for Brace Treatment of Scoliosis

2012· article· en· W2151255424 on OpenAlexaff
Eric Chalmers, Edmond Lou, Doug Hill, Vicky Zhao, Man Sang Wong

Bibliographic record

VenueIEEE Transactions on Neural Systems and Rehabilitation Engineering · 2012
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsAlberta Health ServicesUniversity of Alberta
Fundersnot available
KeywordsBraceScoliosisBracingMedicinePhysical therapyEngineeringSurgeryStructural engineering

Abstract

fetched live from OpenAlex

Bracing is a common nonsurgical treatment for scoliosis, but its effectiveness has been debated. Some clinical studies have shown efficacy of brace treatment is correlated to how the brace has been worn. The more often the patients wear their braces to the prescribed tightness as well as the prescribed length of wear each day, the better the treatment outcome. A system of four wireless pressure control devices was developed to understand brace wear-time and regulate a target pressure range at the brace-body interface. Each pressure control device could function independently and be embedded in the brace at key pressure areas. Such a system could improve the quality of brace wear-making the treatment more effective and refining our understanding of the three-pressure-point brace treatment concept during daily activities. This paper reports the system development and validation. The system was tested on four healthy subjects for 2 h without pressure regulation and 2 h with regulation. The results show that the pressure regulation doubled the time spent in a desired pressure range on average (from 31% to 62%). Brace-wear time was logged correctly. The system was also validated through a seven-day continuous test, and a fully charged battery could run for 30 days without requiring recharge.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.410

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 head0.017
GPT teacher head0.256
Teacher spread0.239 · 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 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

Citations19
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Neural Systems and Rehabilitation EngineeringSame topicScoliosis diagnosis and treatmentFrench-language works237,207