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Record W4403466196 · doi:10.1016/j.jocd.2024.101535

Canadian Adult Reference Data for Body Composition, Trabecular Bone Score and Advanced Hip Analysis Using DXA

2024· article· en· W4403466196 on OpenAlexafffundabout
Lauren A. Burt, Liam T Pond, Annabel R Bugbird, David A. Hanley, Steven K. Boyd

Bibliographic record

VenueJournal of Clinical Densitometry · 2024
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsUniversity of CalgaryAlberta Bone and Joint Health Institute
FundersCanadian Institutes of Health Research
KeywordsMedicineTrabecular boneTrabecular bone scoreBone densityInternal medicineQuantitative computed tomographyOsteoporosis

Abstract

fetched live from OpenAlex

DXA-derived reference data for visceral adipose tissue (VAT) and advanced hip analysis (AHA) parameters spanning the entire adult lifespan are limited. The purpose of this study was to develop age-, site- and sex-specific reference data for dual X-ray absorptiometry (DXA) -derived body composition, trabecular bone score (TBS) and advanced hip analysis (AHA) parameters across the adult lifespan. Adults (N = 908; female: 561 and male: 347) from Calgary and the surrounding area over the age of 20 years participated in this study. Participants received DXA scans of their hip (total hip [TH] and femoral neck [FN]), lumbar spine [LS], forearm [33 % site] and total body (iDXA, GE Lunar, GE Healthcare). Areal bone mineral density (aBMD, g/cm 2 ) was captured at all sites, and body composition variables, including lean mass, fat mass and percent fat, were analyzed from the total body scan. VAT mass was assessed from total body DXA scans. Advanced hip analysis (AHA) was performed on hip scans and trabecular bone score (TBS) on the LS scans to assess bone quality. Site- and sex-specific centile curves and tables were generated using the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) method. Clinicians and researchers can use these Canadian reference data as a tool to assess body composition, TBS and AHA parameters across the adult lifespan.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.173
GPT teacher head0.503
Teacher spread0.330 · 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 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
Published2024
Admission routes3
Has abstractyes

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