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Record W4382010505 · doi:10.1177/08968608231181264

Bones of contention: Predicting and preventing fractures in patients receiving peritoneal dialysis

2023· article· en· W4382010505 on OpenAlexaff
Andrea Cowan, Tayyab Khan, Jenny Thain

Bibliographic record

VenuePeritoneal Dialysis International · 2023
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsLawson Health Research InstituteWestern University
Fundersnot available
KeywordsMedicinePeritoneal dialysisDenosumabBone mineralOsteoporosisDialysisTeriparatideBone remodelingPopulationIntensive care medicineSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Low bone density is common among those individuals receiving peritoneal dialysis. While cross-sectional studies support an association between low bone mineral density (BMD) and prevalent fracture, relying on bone density alone, particularly at the lumbar spine and in those with high degrees of hyperparathyroidism may underestimate fracture risk. Commonly used risk calculators in the general population have been shown to perform reasonably well in those receiving dialysis although they do not include any risk factors for high turnover bone disease that may play a role in increased fracture risk. The best options for decreasing fracture risk in patients receiving peritoneal dialysis are unclear. The evidence for bisphosphonates is limited to small studies of BMD, and concerns about drug accumulation have limited their use. Denosumab is more commonly used and has some evidence for improvement in BMD but carries with it a high risk of hypocalcaemia requiring rigorous prophylaxis. More research is needed to explore practical methods to identify those at risk of fracture and determine the efficacy of antiresorptive and anabolic therapies to decrease this risk.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.013
GPT teacher head0.300
Teacher spread0.287 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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