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Record W2002189920 · doi:10.1111/petr.12365

A longitudinal retrospective analysis of left ventricular mass in a cohort of pediatric kidney transplant recipients

2014· article· en· W2002189920 on OpenAlexaff
Robyn McLaughlin, Lorraine Hamiwka, Susan Samuel, Deborah Fruitman, Silviu Grisaru

Bibliographic record

VenuePediatric Transplantation · 2014
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsMedicineRetrospective cohort studyCohortTransplantationLongitudinal studyKidney transplantationCohort studyInternal medicineKidney diseaseAmbulatoryBody mass indexPediatricsPathology

Abstract

fetched live from OpenAlex

Childhood end-stage kidney disease is associated with increased risk for early adulthood cardiovascular (CV) morbidity and mortality. Increased LVM is an early indicator of CV disease. Previous studies have suggested that LVM decreases after kidney transplantation; however, trends have been inconsistent. A single center retrospective longitudinal cohort analysis of LVM, documented annually, starting before kidney transplantation for up to 10 yr after transplantation was performed. BP documented by annual 24-h ambulatory monitoring studies, and BMI values were also reviewed. Twenty-seven children followed for a mean period of 5.3 yr were included. Depending on definition of LVH, its prevalence pretransplant and in the first years post-transplant was up to 33% dropping to 0-25% thereafter. Individual longitudinal LVM z-score trends were highly variable but generally trended toward the mean immediately after transplant and toward negative values in the following years. BP was stable during the follow-up period while mean annual BMI increased in the first-year post-transplant but declined thereafter. In a cohort of pediatric renal transplant recipients, prevalence of LVH decreased after transplant; however, individual longitudinal LVM trends were highly variable among patients. Prospective studies are needed to correlate individual LVM trends with outcomes.

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)0.000
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 score0.033
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
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 head0.008
GPT teacher head0.240
Teacher spread0.232 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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