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Record W2619039465 · doi:10.1002/jbmr.3182

Osteoporosis in Crisis: It's Time to Focus on Fracture

2017· article· en· W2619039465 on OpenAlexaff
Neil Binkley, Robert D. Blank, William D. Leslie, E. Michael Lewiecki, John A. Eisman, John P. Bilezikian

Bibliographic record

VenueJournal of Bone and Mineral Research · 2017
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsColumbia CollegeUniversity of ManitobaOsteoporosis Canada
Fundersnot available
KeywordsOsteoporosisFocus (optics)Fracture (geology)MedicineInternal medicineEngineeringPhysics

Abstract

fetched live from OpenAlex

A crisis in osteoporosis treatment exists; the majority of those who sustain fracture do not receive treatment to reduce future fracture risk. This crisis presents an opportunity to focus the field from osteoporosis to fracture, the outcome of consequence. Proposed here is a change in focus suggesting that 1) attempts to define the level of trauma leading to fracture are counterproductive and that all fractures in older adults merit consideration of evaluation and 2) bone loss is not the entire problem but rather part of a broader syndrome including osteoporosis, sarcopenia, and other factors leading to fracture. With this approach, all fractures in older adults should be evaluated for potential lifestyle, non-pharmacological, and pharmacological interventions that could be implemented to reduce the risk of fracture recurrence. © 2017 American Society for Bone and Mineral Research.

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)0.007
metaresearch head score (Gemma)0.022
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.019
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0070.009
Scholarly communication0.0100.016
Open science0.0020.008
Research integrity0.0150.029
Insufficient payload (model declined to judge)0.0190.006

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 head0.065
GPT teacher head0.425
Teacher spread0.360 · 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
GenreCommentary

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

Citations82
Published2017
Admission routes1
Has abstractyes

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