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Record W2614240129 · doi:10.2223/jped.1370

Bone mineral density, milk intake and physical activity in boys who suffered forearm fractures

2005· article· en· W2614240129 on OpenAlexaff
Luiz Antônio Simões Pires, Antônio Souza, Orlando Laitano, Flávia Meyer

Bibliographic record

VenueJornal de Pediatria · 2005
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineForearmBone mineralBone densityDensitometryPhysical activityMetaphysisInternal medicineOsteoporosisSurgeryPhysical therapy

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare boys with and without forearm fracture in terms of their bone mineral density, intake of milk and dairy products, and physical activity. METHODS: There were 23 boys in each group (aged between 7 and 13 years). They were submitted to bone densitometry with dual-energy x-ray absorptiometry (DEXA) of the forearm (opposite side of the fracture). Participants answered questionnaires about their intake of milk and dairy products, and about their physical activity. RESULTS: The mean+/-SD of the bone mineral density of the radial and ulnar distal diaphysis in the case group (0.430+/-0.038 g.cm(-2)) was significantly lower (p = 0.018) than that of the control group (0.458+/-0.039 g.cm(-2)). Likewise, the mean of the distal metaphysis of the forearm was 0.309+/-0.033 g.cm(-2) in the case group and 0.349+/-0.054 g.cm(-2) in the control group (p = 0.004). Milk intake (1.5+/-1.2 cups a day) was significantly lower in the case group (p = 0.001) than in the control group (2.7+/-1.2 cups a day). The number of boys who practiced after-school physical activity was significantly lower (p = 0.017) in the case group (six boys = 26%) than in the control group (15 boys = 53%). CONCLUSION: Boys who suffered forearm fracture showed lower bone mineral density compared with the control group. In the case group, milk intake and physical activity were lower than in the control group.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.037
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.012
GPT teacher head0.288
Teacher spread0.277 · 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

Citations10
Published2005
Admission routes1
Has abstractyes

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