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Record W3036198246 · doi:10.1111/apa.15425

Renal length z‐score for the detection of dysfunction in children with solitary functioning kidney

2020· article· en· W3036198246 on OpenAlexafffund
Jaime Manuel Restrepo, Laura Torres-Cánchala, Lina M. Viáfara, Maria A. Agredo, Ana Quintero, Guido Filler

Bibliographic record

VenueActa Paediatrica · 2020
Typearticle
Languageen
FieldMedicine
TopicPediatric Urology and Nephrology Studies
Canadian institutionsWestern University
FundersWestern University
KeywordsMedicineInterquartile rangeRenal functionUrologyCohortReceiver operating characteristicInternal medicineYouden's J statisticMann–Whitney U testOdds ratioRetrospective cohort studyKidneyBody mass indexBiomarkerKidney diseaseProteinuria

Abstract

fetched live from OpenAlex

AIM: To evaluate whether renal length z-scores predict renal dysfunction in children with a solitary functioning kidney (SFK). METHODS: In a single-centre retrospective cohort of children with SFK, we correlated body mass index z-scores, extracellular volume and lean body mass to renal length z-scores. We grouped these z-scores to other markers of renal dysfunction (proteinuria, hypertension, extracellular volume and abnormal estimated glomerular function rate [eGFR]) and analysed renal length z-score with multivariate analysis, receiver-operated characteristics (ROC) plots and Youden's index to determine an appropriate cut-off. RESULTS: , and age at last follow-up 7.4 (3.8-13.4 years). The median renal length z-scores of those without any renal dysfunction (n = 37, 25.1%) were greater (+3.66, interquartile range 3.02-4.47) than those with renal dysfunction (median 3.11, interquartile range 1.76-4.11, P = .0107, Mann-Whitney test). Using a cut-off of z-score of >+1.911, the odds ratio for having no renal dysfunction was 0.07 (95% CI 0.002-0.459, P = .0010). However, accuracy of the renal length z-score was poor (ROC curve 0.6488). CONCLUSION: In this cohort of children with SKF, using the renal length z-score as a biomarker of renal dysfunction at 7 years of age is not recommended.

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 score0.015
Threshold uncertainty score0.323

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 head0.015
GPT teacher head0.218
Teacher spread0.203 · 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

Citations13
Published2020
Admission routes2
Has abstractyes

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