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Record W4252746721 · doi:10.15586/jptcp.v26i4.646

PHARMACOLOGICAL MANAGEMENT OF OSTEOPOROSIS IN POSTMENOPAUSAL WOMEN: THE CURRENT STATE OF THE ART

2019· article· en· W4252746721 on OpenAlexvenueno aff
Davide Gatti, Angelo Fassio

Bibliographic record

VenueJournal of Population Therapeutics and Clinical Pharmacology · 2019
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsnot available
Fundersnot available
KeywordsDenosumabStrontium ranelateTeriparatideOsteoporosisMedicineIntensive care medicineDiscontinuationDiseaseBioinformaticsInternal medicineBone mineral

Abstract

fetched live from OpenAlex

Osteoporosis is a common disease that increases fracture risk. Fragility fractures bring heavy consequences in terms of mortality and disability, with burdensome health and social costs. In subjects with clinical bone fragility, the first goal is to identify the secondary forms of osteoporosis, especially in young subjects, in males and in patients who recently experienced a fragility fracture. In addition, before considering any sort of treatment, it is fundamental to check for adequate calcium and vitamin D intake, since their deficiency is the most common reason for drug failure. In the last decade of the 20th century, several molecules have been developed and proved to be effective in achieving the true goal of any antiosteoporotic drug: fracture prevention. In this article, we considered the most commonly prescribed antiresorptive drugs (hormonal therapy, bisphosphonates, and denosumab), the anabolic agents (teriparatide), the dual-action drugs (romosozumab), and the drugs characterized by an unclear mechanism of action (strontium ranelate) to provide physicians with useful insights for their clinical practice. We discussed the main criteria for the appropriate choice selection and management of each treatment. Finally, we addressed the current controversies related to treatment discontinuation, sequential, and combination therapy.

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)0.002
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 score0.422
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.056
GPT teacher head0.446
Teacher spread0.390 · 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
Published2019
Admission routes1
Has abstractyes

Explore more

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