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Record W2047211237 · doi:10.1080/02841850310003221

Evaluation of bone mineral density of the lumbar spine and proximal femur in population‐based routine health examinations of healthy asians

2004· article· en· W2047211237 on OpenAlexaboutno aff
Wing P. Chan, J.‐F. Liu, Wei-Lain Chi

Bibliographic record

VenueActa Radiologica · 2004
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBone mineralOsteoporosisFemurLumbar spinePopulationMedical recordBone densitySurgeryInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To understand the peak bone mineral density (BMD) and annual loss of BMD of the lumbar spine (L-BMD) and the proximal femur (F-BMD) and the prevalence of osteoporosis in both sexes in Taipei City, Taiwan. MATERIAL AND METHODS: The medical records from a recent 3-year period of annual health examinations at a single institution were reviewed. A total of 1514 men (mean age 49.08 +/- 13.62) and 1955 women (48.07 +/- 14.12), who lived in the Taipei area, had no major systemic disorders, and who had undergone both L-BMD and F-BMD examinations, were recruited. RESULTS: In women, peak L-BMD (1.078 +/- 0.133 g/cm2) occurred in the 30 to 39-year age group, whereas peak F-BMD (0.873 +/- 0.101 g/cm2) occurred in the 17 to 29-year age group. In men, peak L-BMD (1.095 +/- 0.137 g/cm2) and F-BMD (0.989 +/- 0.140 g/cm2) both occurred in the 17 to 29-year age group. The estimated annual bone loss was 0.69% of peak L-BMD in women over 50 years; in the proximal femur this was 0.688% in women and 0.332% in men. In women over 60 years, approximately half of the population had osteoporosis in the lumbar spine. CONCLUSION: We report descriptive BMD data of a Chinese population recruited from a Taipei urban area who underwent routine health examination. The values are similar to or higher than those of Japanese, Canadian and Greek populations. L-BMD was lower than that in the Lebanese, and F-BMD of both sexes was lower than that reported in the Greek study.

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)0.001
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 score0.026
Threshold uncertainty score0.277

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.059
GPT teacher head0.364
Teacher spread0.305 · 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

Citations16
Published2004
Admission routes1
Has abstractyes

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