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Record W4309043872 · doi:10.47750/pnr.2022.13.s06.456

Bone Mineral Density and Body Mass Index: The Practicable Interaction Between Bone Fragility and Obesity Interaction

2022· article· en· W4309043872 on OpenAlexaff
Maryam Inayat, Zuneera Akram, Syed Ahmed Hussain, Usman Ghani Farooqi, Sobia Akhter, Mobeen Islam, Safoora Tariq, Muzammil Hussain

Bibliographic record

VenueJournal of Pharmaceutical Negative Results · 2022
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsUnderweightBone mineralMedicineBody mass indexOverweightOsteopeniaOsteoporosisObesityBone densityCorrelationInternal medicineDemographyMathematics

Abstract

fetched live from OpenAlex

Background: Previous findings have shown that the body mass index (BMI) is related positively to bone mineral density (BMD). In patients with low BMI (<18.5 kg/m2), the levels of BMD have usually been decreased and the T-values have been low. The goal of the study is to assess weight-BMI-BMD relationships among743 healthy people from the Karachi Gulshan district.Methodology: The research comprised a population of 743 people classified into four BMI classes. The BMD measurement in all the study participants was done by using a Sonost 3000 (Ultrasound Bone Densiometer) from Osteosys CO. Ltd. Korea.Results: The findings of BMI and BMD correlation indicated that osteopenia was occur more in underweight individuals than the overweight and obese, while osteoporosis occurred more in those who were obese in the comparison under & overweight individuals.Conclusion: The study of the associations between BMI and BMD in both male and female participants revealed a strong positive association showed by Pearson's correlation analysis.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
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 integrity0.0000.003
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 head0.066
GPT teacher head0.418
Teacher spread0.352 · 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.

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
Published2022
Admission routes1
Has abstractyes

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