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Record W1670736147 · doi:10.1155/2015/819520

The Effects of Elk Velvet Antler Dietary Supplementation on Physical Growth and Bone Development in Growing Rats

2015· article· en· W1670736147 on OpenAlexafffund
Jiongran Chen, Yanfei Yang, Sepideh Abbasi, Daryoush Hajinezhad, Saija Kontulainen, Ali Honaramooz

Bibliographic record

VenueEvidence-based Complementary and Alternative Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaSaskatchewan Health Research Foundation
KeywordsAntlerAlkaline phosphataseInternal medicineEndocrinologyQuantitative computed tomographyBone growthFemurBone mineralBone developmentMedicineBiologyPhysiologyOsteoporosisSurgery

Abstract

fetched live from OpenAlex

Elk velvet antler (EVA) has been used in traditional Oriental medicine for centuries to promote general health; however, little evidence for its effect on bone development is available. We investigated the effects of lifelong exposure of Wistar rats to a diet containing 10% EVA on physical growth and bone development. Measurements included weekly body weights, blood chemistry and kidney and testis/ovary indices (sacrificed at 5, 9, or 16 weeks of age), and bone traits of the femur bones by peripheral quantitative computed tomography (pQCT). Mean body weights were higher in the EVA group at 4-8 weeks in males and at 5 weeks of age in females. The kidney indices were greater in EVA dietary supplemented male rats at 5 and 16 weeks of age, in females at 16 weeks of age, and testis/ovary indices at 5 weeks of age. The femoral length was increased in both males and females at 5 weeks, and several pQCT-measured parameters had increased in EVA males and females. The activity of alkaline phosphatase (ALP) increased in EVA group while the content of calcium and phosphorus did not differ among groups. Our results seem to support a role for dietary supplementation of EVA on growth and bone development in this model.

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)0.000
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 score0.178
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.085
GPT teacher head0.351
Teacher spread0.266 · 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

Citations15
Published2015
Admission routes2
Has abstractyes

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