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Record W2283516282 · doi:10.2215/cjn.08110715

External Validation of the Kidney Failure Risk Equation and Re-Calibration with Addition of Ultrasound Parameters

2016· article· en· W2283516282 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2016
Typearticle
Languageen
FieldMedicine
TopicRenal and Vascular Pathologies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineUltrasoundCalibrationUrologyRadiologyStatistics

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Progression of CKD toward ESRD is heterogeneous. The Kidney Failure Risk Equation (KFRE) was developed to identify CKD patients at high risk of ESRD. We aimed to externally validate KFRE and to test whether the addition of predefined Duplex ultrasound markers - renal resistive index (RRI) or difference of resistive indices in spleen and kidney (DI-RISK) - improved ESRD prediction. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: The prospective Cardiovascular and Renal Outcome in CKD 2-4 Patients-The Fourth Homburg evaluation (CARE FOR HOMe) study recruits CKD stage G2-G4 patients referred to a tertiary referral center for nephrologic care. Four hundred three CARE FOR HOMe participants enrolled between 2008 and 2012 had available RRI measurements at study inclusion; they were subsequently followed for a mean of 4.4±1.6 years. This subcohort was used to validate KFRE and to assess the added value of the ultrasound markers (new models KFRE+RRI and KFRE+DI-RISK). Model performance was assessed by log-likelihood ratio test, c-statistic, integrated discrimination improvement metrics (for study participants without subsequent ESRD [IDI No ESRD] and for patients with ESRD [IDI ESRD]), and calibration plots. If either new model improved on KFRE, we determined to validate it in an independent cohort of 162 CKD patients. RESULTS: KFRE predicted ESRD in CARE FOR HOMe participants with a c-statistic of 0.91 (95% confidence interval, 0.83 to 0.99). Adding RRI improved the KFRE model (P<0.001), and the KFRE+RRI model was well calibrated; however, the c-statistic (0.91 [0.83-1.00]) was similar, and overall sensitivity (IDI No ESRD=0.05 [0.00-0.10]) or overall specificity (IDI ESRD=0.00 [0.00-0.01]) did not improve. Adding DI-RISK did not improve the KRFE model. In the external validation cohort, we confirmed that the KFRE+RRI model did not outperform KFRE. CONCLUSIONS: Routine Duplex examinations among CKD patients did not improve risk prediction for progression to ESRD beyond a validated equation.

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.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.326
Threshold uncertainty score0.703

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.040
GPT teacher head0.312
Teacher spread0.272 · 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