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Record W2984402296 · doi:10.1109/tim.2002.807792

Low power wireless load monitoring system for the treatment of scoliosis

2002· article· en· W2984402296 on OpenAlexaff
Edmond Lou, James Raso, Doug Hill, N.G. Durdle

Bibliographic record

VenueIEEE Transactions on Instrumentation and Measurement · 2002
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsGlenrose Rehabilitation Hospital
Fundersnot available
KeywordsBraceScoliosisMicrocontrollerTest (biology)MicrocomputerBattery (electricity)WirelessPower (physics)Computer scienceEngineeringPhysical therapySimulationAutomotive engineeringPhysical medicine and rehabilitationStructural engineeringMedicineElectrical engineeringSurgeryTelecommunications

Abstract

fetched live from OpenAlex

The efficacy of brace treatment for children with abnormal spinal curvature has been hampered by the lack of comprehensive information about wear characteristics. A battery-powered microcomputer system was developed to monitor loads exerted by braces used to treat children with spinal deformities. The system can be used by patients to ensure that the brace is being worn as prescribed, by clinicians to provide a record of how well the brace has been used, and by researchers to investigate brace mechanics. Data acquisition is controlled by a microcontroller and can be sampled with intervals ranging from 1 s to 1 h. In a preliminary study, a subject volunteered to test the system for 1 day. The load level was recorded as 1.20/spl plusmn/0.01 N when the subject was standing normally. The average force for the test day was 1.52/spl plusmn/0.75 N. This study demonstrates the feasibility of the approach, helps patients better wear their braces and increases our understanding of brace mechanics.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.076
GPT teacher head0.287
Teacher spread0.211 · 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 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

Citations3
Published2002
Admission routes1
Has abstractyes

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