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Record W2145244065 · doi:10.2217/ahe.10.42

Drug-Induced Osteoporosis in the Older Adult

2010· article· en· W2145244065 on OpenAlexaff
Mary Beth O’Connell, Laura M. Borgelt, Susan K. Bowles, Sheryl F. Vondracek

Bibliographic record

VenueAging Health · 2010
Typearticle
Languageen
FieldMedicine
TopicPharmacological Effects and Toxicity Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicinePolypharmacyOsteoporosisWarfarinDrugIntensive care medicineBisphosphonateVitamin D and neurologyHormone replacement therapy (female-to-male)PopulationInternal medicinePharmacologyAtrial fibrillation

Abstract

fetched live from OpenAlex

The elderly population is at risk for polypharmacy and, therefore, also at risk for drug-induced osteoporosis (DIOP). Epidemiologic studies provide valuable information about medications that may place patients at risk for DIOP. While glucocorticoids are the most common cause of DIOP, the use of several other therapeutic agents can place patients at risk for significant bone loss and fracture. These medications include, but are not limited to, aromatase inhibitors, gonadotropin-releasing hormone agonists, thyroid replacement therapy, antiepileptics, antidepressants, antipsychotics, lithium, gastric acid lowering agents, thiazolidinediones, loop diuretics, heparins and warfarin, vitamin A and cyclosporine. This article provides information about their mechanism of action, studies that have evaluated these agents in DIOP and prevention and treatment strategies.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.361
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2010
Admission routes1
Has abstractyes

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