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Record W2101897995 · doi:10.1186/1748-7161-9-s1-o10

The ability of surface topography postural measurements to detect cobb angle progression in adolescents with idiopathic scoliosis (AIS) and a main thoracic curve: full torso scans compared to back only parameters

2014· article· en· W2101897995 on OpenAlexaff
Éric Parent, Samantha Chabot, Lindsey Westover, Douglas L. Hill, Marc Moreau, Douglas Hedden, Edmond Lou, Samer Adeeb

Bibliographic record

VenueScoliosis · 2014
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsAlberta Health ServicesUniversity of Alberta
Fundersnot available
KeywordsTorsoIdiopathic scoliosisCobb angleMedicineCobBScoliosisOrthopedic surgeryOrthodonticsBiomechanicsAnatomyBiomedical engineeringSurgeryBiology

Abstract

fetched live from OpenAlex

External deformity due to scoliosis can be quantified by surface topography (ST) from full-torso and back-only scans. Determining the ability of ST parameters to detect which curves remain stable is necessary to determine if ST can help reduce radiation exposure in monitoring scoliosis progression. The study goal was to compare the ability of full-torso and back-only ST parameters to detect which curves do not progress by >5 (Cobb degrees) in AIS with a main thoracic curve.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.031
GPT teacher head0.313
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 teacher head, not a consensus.

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

Citations2
Published2014
Admission routes1
Has abstractyes

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