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Record W2951784778 · doi:10.1093/ndt/gfz106.fp448

FP448QUANTITATIVE ULTRASOUND AND FRACTURES IN EARLY CKD: ANALYSIS OF CARTAGENE

2019· article· en· W2951784778 on OpenAlexaffabout
Louis‐Charles Desbiens, Rémi Goupil, François Madore, Fabrice Mac‐Way

Bibliographic record

VenueNephrology Dialysis Transplantation · 2019
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsHôpital du Sacré-Cœur de MontréalUniversité Laval
Fundersnot available
KeywordsMedicineUltrasoundKidney diseaseRadiologyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: The use of calcaneal quantitative ultrasound (QUS), a non-invasive and accessible imaging modality, has rarely been studied in CKD individuals. Therefore, we aimed to evaluate the association of QUS parameters with the incidence of fractures and assess the predictive value of QUS in early CKD individuals. METHODS: Prospective analysis of CARTaGENE, a cohort of individuals from the province of Quebec (Canada) aged 40 to 69 years recruited between 2009 and 2010. Individuals with eGFR above 30 ml/min/1,73m2 and available QUS measurements are included. The T-score, speed of sound (SOS) and broadband ultrasound attenuation (BUA) are measured by calcaneal QUS (GE Lunar Achilles Express) performed at baseline. Fracture incidence at major osteoporotic (MOF) sites or any anatomical site (any fracture) from recruitment to 2016 is identified using health administrative databases with validated algorithms. Individuals are grouped by CKD stage (non-CKD, stage 2 and stage 3) according to their baseline eGFR. The association between QUS parameters and fracture incidence in each CKD stratum is assessed using Cox regression models with interaction terms for CKD stages. Unadjusted, or fully adjusted (demographics, comorbidities, medication) models are used. Predictive performance of QUS is assessed by computing discrimination (c-statistic) and calibration (predicted/observed ratio [P/O]) in each CKD stratum. RESULTS: We included 18,307 individuals (51% women, mean age 54, mean eGFR 88 ml/min/1,73m2, 47% stage 2, 4% stage 3 CKD). 766 individuals with available eGFR and QUS had a fracture during the follow-up (345 non-CKD, 373 CKD stage 2 and 48 CKD stage 3). QUS T-score was associated with major osteoporotic fractures in each CKD stratum in unadjusted and adjusted models (Adjusted standardized hazard ratio [HR] = 1.86 [1.16 to 2.98] in CKD stage 3; HR = 1.42 [1.19 to 1.69] in CKD stage 2; HR = 1.56 [1.30 to 1.88] in non-CKD; p-value for interaction = 0.491). Similar results were obtained for the association of SOS and BUA with MOF. QUS T-score was associated with any fracture in non-CKD and CKD stage 2 individuals (HR = 1.48 [1.32 to 1.66] and 1.39 [1.24 to 1.55] respectively). There was a tendency for an association between QUS t-score and any fracture in CKD stage 3 (HR= 1.31 [0.97 to 1.78]; p-value for interaction = 0.638). Similar results were obtained for SOS and BUA. Discrimination of MOF and any fracture was similar across CKD strata (Any fracture: c-statistic = 0.62 for stage 3; 0.62 for stage 2; 0.63 for non-CKD). In contrast, QUS measurements underestimated any fracture and MOF risk in stage 3 CKD individuals even after full adjustment (Any fracture: P/O = 0.70 [0.54 to 0.94] in stage 3 CKD; P/O = 0.99 [0.89 to 1.09] in stage 2 CKD; P/O = 1.06 [0.95 to 1.18] in non-CKD). CONCLUSIONS: Calcaneal quantitative ultrasound parameters are associated with fracture incidence in individuals without and with early CKD. While these parameters discriminate fracture incidence in individuals with and without early CKD, they underestimate the incidence of fracture in CKD stage 3 individuals. Abnormal quantitative ultrasound parameters in CKD individuals should therefore be interpreted as an indication of higher fracture 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 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)0.005
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 score0.200
Threshold uncertainty score0.397

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.276
Teacher spread0.268 · 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".

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Citations0
Published2019
Admission routes2
Has abstractyes

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