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Record W3004749891 · doi:10.1002/jbmr.3977

Comparison of Fracture Prediction Tools in Individuals Without and With Early Chronic Kidney Disease: A Population-Based Analysis of CARTaGENE

2020· article· en· W3004749891 on OpenAlexafffund
Louis‐Charles Desbiens, Aboubacar Sidibé, Claudia Beaudoin, Sonia Jean, Fabrice Mac‐Way

Bibliographic record

VenueJournal of Bone and Mineral Research · 2020
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsInstitut National de Santé Publique du QuébecUniversité LavalHôtel-Dieu de Québec
FundersFonds de Recherche du Québec - SantéKidney Foundation of CanadaCanadian Institutes of Health ResearchCanadian Society of NephrologyUniversité Laval
KeywordsKidney diseaseMedicineDiseasePopulationInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Whether fracture prediction tools developed for the management of osteoporosis can be used in chronic kidney disease (CKD) is poorly known. We aimed to compare the performance of fracture prediction tools in non-CKD and CKD. We analyzed CARTaGENE, a population-based survey of 40-year-old to 69-year-old individuals recruited between 2009 and 2010. Renal function was assessed using baseline creatinine and categorized according to Kidney Disease Improving Global Outcomes (KDIGO) guidelines (non-CKD, stage 2, stage 3). Individuals without creatinine measurements or with advanced CKD (stage 4 or 5; prevalence <0.25%) were excluded. Predicted 5-year fracture probabilities (using Fracture Risk Assessment Tool [FRAX], QFracture, and Garvan) were computed at baseline. Fracture incidence (major fracture [MOF] or any fracture) was evaluated in administrative databases from recruitment to March 2016. Discrimination (hazard ratios [HRs] per standard deviation [SD] increase in Cox models; c-statistics) and calibration (standardized incidence ratios [SIRs] before and after recalibration) were assessed in each CKD strata. We included 19,393 individuals (9522 non-CKD; 9114 stage 2; 757 stage 3). A total of 830 patients had any fracture during follow-up, including 352 MOF. FRAX (HR = 1.89 [1.63-2.20] non-CKD; 1.64 [1.41-1.91] stage 2; 1.76 [1.10-2.82] stage 3) and QFracture (HR = 1.90 [1.62-2.22] non-CKD; 1.57 [1.35-1.82] stage 2; 1.86 [1.19-2.91] stage 3) discriminated MOF similarly in non-CKD and CKD. In contrast, the discrimination of Garvan for any fracture tended to be lower in CKD stage 3 compared to non-CKD and CKD stage 2 (HR = 1.36 [1.22-1.52] non-CKD; 1.34 [1.20-1.50] stage 2; 1.11 [0.79-1.55] stage 3). Before recalibration, FRAX globally overestimated fracture risk while QFracture and Garvan globally underestimated fracture risk. After recalibration, FRAX and QFracture were adequately calibrated for MOF in all CKD strata whereas Garvan tended to underestimate any fracture risk in CKD stage 3 (SIR = 1.31 [0.95-1.81]). In conclusion, the discrimination and calibration of FRAX and QFracture is similar in non-CKD and CKD. Garvan may have a lower discrimination in CKD stage 3 and underestimate fracture risk in these patients. © 2020 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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.078
GPT teacher head0.419
Teacher spread0.341 · 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 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
Published2020
Admission routes2
Has abstractyes

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