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Record W3188231199 · doi:10.1002/hsr2.346

Diastolic hypertension is associated with proteinuria in pediatric patients

2021· article· en· W3188231199 on OpenAlexaff
Robert L. Myette, Dylan Burger, Pavel Geier, Janusz Feber

Bibliographic record

VenueHealth Science Reports · 2021
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsOttawa HospitalChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsProteinuriaBlood pressureMedicineInternal medicineCardiologyPrehypertensionDiastoleCreatinineHeart rateKidney

Abstract

fetched live from OpenAlex

Abstract Background and Aims Blood pressure lability has been observed in certain cohorts of pediatric patients with variable degrees of proteinuria; however, the impact of proteinuria on blood pressure is not fully elucidated. The objective of our study was to analyze blood pressure and heart rate in pediatric patients with proteinuria. Methods We performed a retrospective chart review of patients (age 1‐18) diagnosed with idiopathic nephrotic syndrome, with varying degrees of proteinuria. Blood pressure and heart rate data were analyzed in relation to anthropometric and biochemical parameters. A total of 72 urine sample analyses, along with associated blood pressure measurements, were obtained from the charts of 33 children (males = 25). Results Diastolic blood pressure Z ‐scores were significantly higher in proteinuric patients (urine protein/creatinine >0.02 g/mmol) compared to non‐proteinuric patients ( P = .006; Cohen‐ d 0.97 [0.41; 1.53]). Systolic blood pressure was also significantly higher in proteinuric patients ( P = .04), but with a less significant effect size (Cohen‐ d 0.54 [−0.002; 1.08]). Proteinuria (>0.02 g/mmol) was the most significant predictor of diastolic ( β = .79, P = .04), but not systolic blood pressure elevation on multivariate analysis. Conclusions We observed a disproportionate increase in diastolic blood pressure vs systolic blood pressure in patients with proteinuria.

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)0.001
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 score0.005
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.038
GPT teacher head0.298
Teacher spread0.260 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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