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Record W2048415538 · doi:10.4021/jem.v2i3.104

The Effects of Supraphysiologic Doses of Vitamin D3 in Conjunction With Teriparatide on Bone Mineral Density in Two Postmenopausal Females With Severe Osteoporosis

2012· article· en· W2048415538 on OpenAlexvenueno aff
Pooja Raghavan, Elena Christofides

Bibliographic record

VenueJournal of Endocrinology and Metabolism · 2012
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTeriparatideMedicineOsteoporosisBone mineralVitamin D and neurologyInternal medicineEndocrinologyPostmenopausal womenPostmenopausal osteoporosisVitaminBone density

Abstract

fetched live from OpenAlex

Teriparatide was approved by the FDA in 2002 for the treatment of osteoporosis in postmenopausal women who are at high risk for fractures. Previous studies involving the use of teriparatide in conjunction with vitamin D and calcium supplementation have suggested that sufficient vitamin D levels may not be a requirement to achieve an effective response to teriparatide. We present a case of two postmenopausal females with severe osteoporosis who were treated with teriparatide along with calcium and supraphysiologic doses of vitamin D, as illustrated by an increase from their baseline serum 25-hydroxyvitamin D concentrations. Both patients experienced an increase in bone mineral density that was significantly higher than what has been seen in prior studies of teriparatide administration in conjunction with physiologic doses of vitamin D. These findings suggest that administering supraphysiologic doses of vitamin D may in fact potentiate the effects of teriparatide in postmenopausal females with osteoporosis, resulting in larger increases in bone mineral density than would otherwise be expected. doi:10.4021/jem104 w

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.014
GPT teacher head0.292
Teacher spread0.278 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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
Published2012
Admission routes1
Has abstractyes

Explore more

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