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Record W2792112123 · doi:10.1097/mnh.0000000000000411

Fracture risk and treatment in chronic kidney disease

2018· review· en· W2792112123 on OpenAlexaff
Kelsey Connelly, David Collister, Navdeep Tangri

Bibliographic record

VenueCurrent Opinion in Nephrology & Hypertension · 2018
Typereview
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsUniversity of ManitobaMcMaster UniversitySeven Oaks General Hospital
Fundersnot available
KeywordsMedicineKidney diseaseDiseaseIntensive care medicineFracture (geology)Internal medicineBiology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Chronic kidney disease (CKD) is associated with the development of mineral bone disorder (MBD), osteoporosis, and fragility fractures. The purpose of this review is to provide an update on recent findings in the diagnosis and treatment of osteoporosis in patients with CKD. RECENT FINDINGS: Multiple observational studies have shown that bone mineral density measurement using DEXA is equally predictive in CKD stages 1-3, as in the general population. Post hoc analyses from randomized trials of bisphosphonates, SERM, RANKL inhibitors and PTH agonists all suggest equal efficacy in mild-moderate CKD. A recent systematic review also found evidence for efficacy of bisphosphonates in patients with a kidney transplant. SUMMARY: Bone mineral density measurement using DEXA is accurate in patients with CKD stages 1-3 and should be considered to guide treatment of osteoporosis. Current treatments are unaffected by mild-to-moderate decline in kidney function, and physicians should use bisphosphonates and other osteoporosis treatments in this population, whenever indicated. Studies evaluating the optimal diagnostic and management strategy in patients with CKD stages (G4-5D) are needed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.919
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
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 head0.075
GPT teacher head0.374
Teacher spread0.299 · 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.

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

Citations23
Published2018
Admission routes1
Has abstractyes

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