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Thrombotic Complications of Neonates and Children with Congenital Nephrotic Syndrome

2014· article· en· W2337970808 on OpenAlexaff
Keith K. Lau, Howard H.W. Chan, Patti Massicotte, Anthony K.C. Chan

Bibliographic record

VenueCurrent Pediatric Reviews · 2014
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsHamilton Health Sciences
Fundersnot available
KeywordsMedicineHypoalbuminemiaNephrotic syndromeCongenital nephrotic syndromePediatricsIntensive care medicineDiseaseProteinuriaComplicationIncidence (geometry)HyperlipidemiaThrombophiliaSurgeryInternal medicineThrombosisDiabetes mellitus

Abstract

fetched live from OpenAlex

Congenital nephrotic syndrome (CNS) refers to a disease presenting with massive proteinuria in association with hypoalbuminemia, hyperlipidemia, and edema at birth or within the first three months of life. In the past, most children with CNS had extremely poor prognosis and succumbed to various complications, usually within the first 6 months. Recent advancements in protein supplementation and nutritional support, renal replacement therapy and renal transplantation in infancy, render these patients to have much better outcomes. However, there are still many hurdles in the management of this disease. Thromboembolism is an uncommon, yet important complication which the healthcare givers must be aware of. This article reviews the challenges in the management of the thrombotic complications with special emphasis on the unique characteristics of the newborn hemostasis system and anti-thrombin (AT) depletion in nephrotic syndrome. Due to the relatively low incidence of CNS in children and scarce information in the literature on the optimal management of the thromboembolic complications, most of the recommendations are based on the authors' experience.

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)0.000
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.285
Teacher spread0.262 · 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
GenreReview

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

Citations12
Published2014
Admission routes1
Has abstractyes

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