MétaCan
Menu
← Back to cohort
Record W7161988604 · doi:10.82308/25305

Pediatric data for normal distal femur bone mineral density in children aged 3 to 18 years using the lunar prodigy densitometer

2018· dissertation· en· W7161988604 on OpenAlexaboutno aff
Mélissa Fiscaletti

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsnot available
Fundersnot available
KeywordsBone mineralAnthropometryFemurBone densityOsteoporosisScoliosisLumbar spineDensitometer

Abstract

fetched live from OpenAlex

Objectives: Children with compromised weight bearing and limited mobility due to neuromuscular impairments are particularly at risk for low trauma long bone fractures. Whilst dual-energy absorptiometry scan (DXA) is a useful tool to assess areal bone mineral density (aBMD) in children, several obstacles can impede proper assessment of whole body (WB) or lumbar spine (LS) aBMD in this population. Musculo-skeletal deformities and orthopaedic hardware can render measurement of aBMD at these sites uninterpretable or impossible. For example, measurement of LS aBMD in a child with a neuromuscular scoliosis may be technically infeasible and clinically invalid if orthopaedic hardware is located in the region of interest. The lateral distal femur (LDF) has been proposed as an alternative site for aBMD assessment. Furthermore, distal femur fractures are common in this population, making LDF aBMD assessment clinically relevant. There are few normative data for aBMD at the LDF in children. This study aimed to construct normative centile curves for LDF aBMD using data from healthy children aged 3-18 years using the Lunar Prodigy DXA scan. Methods: This was a cross-sectional assessment of 241 healthy Canadian children (48% males, near 89% Caucasian) ranging in age from 3-18 years. aBMD measures were completed at 3 different regions of the LDF ("region 3, 4 and 5"), LS and WB using the Lunar Prodigy Densitometer. Age was chosen as the scaling variable and sex-specific reference curves for each of the 3 LDF regions were generated using LMS-ChartMaker Pro. LDF aBMD-for-age Z-scores were calculated then compared to WB and LS aBMD and anthropometric data by correlation analysis. Linear regression and ANOVA studies were used to examine predictors associated with aBMD at the different LDF regions. Results: LDF aBMD measurements were progressively higher with older age (R2 = 0.64 Region 3; R2 = 0.77 region 4; R2 = 0.83 region 5; p-value < 0.0001 for all regions) and were highly correlated with height and weight (R2 ≥ 0.60, p-value <0.0001or all regions). Furthermore, LDF aBMD was highly correlated with WB (R2 ≥ 0.74; p-value <0.0001 for all regions) and LS aBMD measurements (R2 ≥ 0.71; p-value < 0.0001for all regions). LDF region 3 Z-scores correlated best with LS aBMD Z-scores (R2 = 0.36; p-value < 0.0001) while region 5 Z-scores correlated best with WB aBMD Z-scores (R2 ≥ 0.41; p-value < 0.0001). At each femoral region, sex differences in aBMD depended significantly on age (p-value for interaction ≤ 0.005).Conclusion: This is the first study to provide normative data for LDF aBMD in children between the ages of 3 and 18 years, using the Lunar Prodigy DXA scan.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.665
Threshold uncertainty score0.674

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.057
GPT teacher head0.384
Teacher spread0.327 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same topicBone health and osteoporosis research→French-language works237,207→