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Record W3033002973 · doi:10.1111/petr.13745

Physical activity and its correlates in a pediatric solid‐organ transplant population

2020· article· en· W3033002973 on OpenAlexaff
Samantha Lui, Astrid M. De Souza, Atul Sharma, Julie Fairbairn, Richard A. Schreiber, Kathryn Armstrong, Tom Blydt‐Hansen

Bibliographic record

VenuePediatric Transplantation · 2020
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversity of ManitobaBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineDemographicsPopulationRenal transplantKidney transplantPediatricsRetrospective cohort studyInternal medicineKidney transplantationTransplantationDemographyEnvironmental health

Abstract

fetched live from OpenAlex

PA has been shown to have benefits in SOT patients. Studies assessing physical activity levels and its correlates in a pediatric solid-organ transplant population are limited. The aim of this study was to assess PA levels and identify baseline and contemporaneous factors that contribute to PA in a pediatric SOT population. A retrospective cross-sectional review was performed on 58 pediatric transplant patients (16 heart, 29 kidney, and 13 liver transplant). PA was measured by PAQ-C or PAQ-A. Demographics, baseline, and contemporaneous factors were collected. There were no significant differences in baseline and contemporaneous characteristics between heart, kidney, and liver transplant recipients. SOT recipients were 15.2 [12.3-17.3] years old at time of completing the PAQ. Median PAQ score was 2.2 [1.7-2.9]. There were no significant differences in PAQ scores between organ transplant type or between genders. Lower PAQ score was associated with sensory disability (9 vs 49 without disability; P = <.01) and age at time of completing the PAQ (r = -.50, P = <.01). These results suggest that older age at time of completing the PAQ and presence of sensory disability may influence PA levels in the pediatric SOT population.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.377
Teacher spread0.325 · 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.

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

Citations10
Published2020
Admission routes1
Has abstractyes

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