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Record W2010704299 · doi:10.1089/15246090050200024

The Effect of an Ipriflavone-Containing Supplement on Urinary N-Linked Telopeptide Levels in Postmenopausal Women

2000· article· en· W2010704299 on OpenAlexaff
Andrew D. Halpner, Gottfried Kellermann, Marla J. Ahlgrimm, Carol L. Arndt, Naeem A. Shaikh, Jeffrey J. Hargrave, Peter G. Tallas

Bibliographic record

VenueJournal of Women s Health & Gender-Based Medicine · 2000
Typearticle
Languageen
FieldMedicine
TopicPhytoestrogen effects and research
Canadian institutionsDouglas College
Fundersnot available
KeywordsPlaceboN-terminal telopeptideMedicineOsteoporosisHormone replacement therapy (female-to-male)Vitamin D and neurologyInternal medicineUrinary systemBone remodelingMenopauseEndocrinologyPopulationUrinary calciumPostmenopausal womenPhysiologyUrologyOsteocalcinChemistryTestosterone (patch)PathologyAlkaline phosphatase

Abstract

fetched live from OpenAlex

Osteoporosis is a significant health concern to our aging population. We report here the results of a pilot placebo-controlled trial of a dietary supplement containing ipriflavone, calcium, and vitamin D on a urinary marker of bone breakdown in postmenopausal women. Seven postmenopausal women not currently receiving hormone replacement therapy received either an ipriflavone-containing supplement or placebo for 3 months. Urinary N-linked telopeptides, a marker of bone breakdown, declined by 29% in those receiving the supplement, whereas an increase in this marker was observed in the group receiving the placebo. No changes were observed in salivary hormone measurements. Although our sample size was small, to the best of our knowledge, this is the first report that demonstrates changes in N-linked telopeptide levels as a result of consuming an ipriflavone-containing product. Our findings confirm those of other researchers that demonstrate the usefulness of ipriflavone at slowing the progression of bone loss and suggest that measuring N-linked telopeptides may be a useful tool to assess therapeutic efficacy.

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)0.014
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.678
Threshold uncertainty score0.892

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.384
Teacher spread0.342 · 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 designOther design
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

Citations7
Published2000
Admission routes1
Has abstractyes

Explore more

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