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Record W4285395433 · doi:10.1097/mpg.0000000000003560

Health Care Transition for Adolescents and Young Adults With Pediatric‐Onset Liver Disease and Transplantation

2022· article· en· W4285395433 on OpenAlexaff
Jennifer Vittorio, Beverly Kosmach‐Park, Lindsay Y. King, Ryan T. Fischer, Emily M. Fredericks, Vicky L. Ng, Amrita Narang, Sara K. Rasmussen, John C. Bucuvalas

Bibliographic record

VenueJournal of Pediatric Gastroenterology and Nutrition · 2022
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineHepatologyLiver transplantationTransplantationPediatric gastroenterologyLiver diseaseAdult careHealth carePublic healthYoung adultPosition paperIntensive care medicineMultidisciplinary approachFamily medicineMedical homePediatricsDiseaseInternal medicineNursingPathology

Abstract

fetched live from OpenAlex

Advances in medical therapies and liver transplantation have resulted in a greater number of pediatric patients reaching young adulthood. However, there is an increased risk for medical complications and morbidity surrounding transfer from pediatric to adult hepatology and transplant services. Health care transition (HCT) is the process of moving from a child/family-centered model of care to an adult or patient-centered model of health care. Successful HCT requires a partnership between pediatric and adult providers across all disciplines resulting in a transition process that does not end at the time of transfer but continues throughout early adulthood. Joint consensus guidelines in collaboration with the American Society of Transplantation are presented to facilitate the adoption of a structured, multidisciplinary approach to transition planning utilizing The Six Core Elements of Health Care Transition TM for use by both pediatric and adult specialists. This paper provides guidance and seeks support for the implementation of an HCT program which spans across both pediatric and adult hepatology and transplant centers.

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 score0.024
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.015
GPT teacher head0.305
Teacher spread0.290 · 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

Citations25
Published2022
Admission routes1
Has abstractyes

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