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Record W4404300158 · doi:10.1016/j.jocd.2024.101547

Associations between Bone Mineral Density and WOMAC Scores in Healthy Individuals: Insights from the Qatar Biobank

2024· article· en· W4404300158 on OpenAlexaboutno aff
Mohammed Al‐Hamdani, Saja A Abdallah, Amal Al-Haidose, Atiyeh M. Abdallah

Bibliographic record

VenueJournal of Clinical Densitometry · 2024
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsnot available
FundersQatar University
KeywordsMedicineBiobankBone mineralWOMACPhysical therapyEnvironmental healthAlternative medicineInternal medicineBioinformaticsOsteoporosisPathologyOsteoarthritis

Abstract

fetched live from OpenAlex

BACKGROUND: Bone mineral density (BMD) is an indicator of bone health that predicts future bone fractures. The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) is used to assess the severity of symptoms related to pain, stiffness, and function in diseased hip and knee joints. Here we assessed whether BMD measured at specific sites predicts WOMAC scores in healthy individuals whilst controlling for sociodemographic variables. METHODOLOGY: BMD, sociodemographic, and WOMAC data were collected from 1764 healthy adult individuals attending the Qatar Biobank (QBB). Multiple linear regression was used to examine associations between sociodemographic factors, total body BMD and BMD related to specific skeletal sites (including femoral (femoral neck and trochanter), trunk, pelvis and total spine), and the three WOMAC subscales (pain, stiffness, and physical function). RESULTS: = 0.091, ΔF(7, 1724) = 3.762, p < 0.001) after controlling for sociodemographic variables, with a higher total body BMD associated with an increase in compromised physical function. CONCLUSION: Total body BMD seems to be a more important predictor of stiffness and physical function than specific skeletal sites BMD measures. Monitoring BMD may have important implications for osteoarthritis patients.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.024
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
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 integrity0.0000.002
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 head0.108
GPT teacher head0.463
Teacher spread0.356 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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