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Record W2146172130 · doi:10.1155/2012/672403

Normative Data for Bone Mass in Healthy Term Infants from Birth to 1 Year of Age

2012· article· en· W2146172130 on OpenAlexafffundabout
Sina Gallo, Catherine A. Vanstone, Hope A. Weiler

Bibliographic record

VenueJournal of Osteoporosis · 2012
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsUniversity of ManitobaMcGill University
FundersCanadian Institutes of Health Research
KeywordsMedicineBone mineralDual-energy X-ray absorptiometryBone mineral contentLumbar spineBone healthFemurGestational agePediatricsOsteoporosisNuclear medicineSurgeryInternal medicinePregnancy

Abstract

fetched live from OpenAlex

For over 2 decades, dual-energy X-ray absorptiometry (DXA) has been the gold standard for estimating bone mineral density (BMD) and facture risk in adults. More recently DXA has been used to evaluate BMD in pediatrics. However, BMD is usually assessed against reference data for which none currently exists in infancy. A prospective study was conducted to assess bone mass of term infants (37 to 42 weeks of gestation), weight appropriate for gestational age, and born to healthy mothers. The group consisted of 33 boys and 26 girls recruited from the Winnipeg Health Sciences Center (Manitoba, Canada). Whole body (WB) as well as regional sites of the lumbar spine (LS 1-4) and femur was measured using DXA (QDR 4500A, Hologic Inc.) providing bone mineral content (BMC) for all sites and BMD for spine. During the year, WB BMC increased by 200% (76.0 ± 14.2 versus 227.0 ± 29.7 g), spine BMC by 130% (2.35 ± 0.42 versus 5.37 ± 1.02 g), and femur BMC by 190% (2.94 ± 0.54 versus 8.50 ± 1.84 g). Spine BMD increased by 14% (0.266 ± 0.044 versus 0.304 ± 0.044 g/cm(2)) during the year. This data, representing the accretion of bone mass during the first year of life, is based on a representative sample of infants and will aid in the interpretation of diagnostic DXA scans by researchers and health professionals.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.079
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.071
GPT teacher head0.383
Teacher spread0.312 · 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

Citations37
Published2012
Admission routes3
Has abstractyes

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