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Record W4389514728 · doi:10.55927/ijis.v2i11.6179

Skeletal Effects of Soy Isoflavone in Humans: Bone Mineral Density and Bone Markers

2023· article· en· W4389514728 on OpenAlexfundno aff
Rehan Haider

Bibliographic record

VenueInternational Journal of Integrative Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicPhytoestrogen effects and research
Canadian institutionsnot available
FundersUniversity of KarachiUniversity of Calgary
KeywordsDeoxypyridinolineBone remodelingIsoflavonesBone mineralBone resorptionEndocrinologyOsteocalcinInternal medicineOsteoporosisEstrogenBone densityMedicineChemistryAlkaline phosphataseBiochemistryEnzyme

Abstract

fetched live from OpenAlex

The potential skeletal effects of soy isoflavones in humans have garnered significant interest owing to their structural resemblance to endogenous estrogen and their potential to impact bone health. This abstract provides a concise overview of the current understanding of the effects of soy isoflavones on bone mineral density (BMD) and bone markers. Numerous studies have investigated the relationship between soy isoflavone consumption and BMD. Some studies have suggested a positive association between soy isoflavone intake and BMD, particularly among postmenopausal women. Isoflavones may exert their effects through estrogen receptor-mediated pathways, potentially mitigating bone loss by reducing osteoclastic activity and promoting osteoblastic functions. However, conflicting results have been reported, with certain studies demonstrating no significant impact on BMD. In addition to BMD, bone markers such as serum osteocalcin, urinary deoxypyridinoline, and tartrate-resistant acid phosphatase have been evaluated to elucidate the mechanistic effects of soy isoflavones on bone metabolism. These markers provide insights into bone turnover, resorption, and formation. Clinical trials have reported mixed findings regarding the influence of soy isoflavones on bone markers, reflecting the complexity of their interaction with bone physiology

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
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 head0.017
GPT teacher head0.354
Teacher spread0.336 · 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
Published2023
Admission routes1
Has abstractyes

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