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Record W2937546269 · doi:10.1097/tp.0000000000002749

Digital Wings: Innovations in Transplant Readiness for Adolescent and Young Adult Transplant Recipients

2019· article· en· W2937546269 on OpenAlexaff
Douglas Mogul, Emily M. Fredericks, Tammy M. Brady, Tamir Miloh, Kristin A. Riekert, Natalie Williams, Ryan Ford, Michael Fergusson, Beverly Kosmach‐Park, Jon Hochstein, Gayathri Naraparaju, Macey L. Henderson, Dorry L. Segev, John F. P. Bridges

Bibliographic record

VenueTransplantation · 2019
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsAyogo (Canada)
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesAgency for Healthcare Research and Quality
KeywordsMedicineTransplantationHealth carePopulationGerontologySurgeryEnvironmental healthPolitical science

Abstract

fetched live from OpenAlex

The Johns Hopkins University School of Medicine organized 2 multistakeholder symposia on February 2, 2018 and January 11, 2019 to address the problem of high graft failure in adolescent and young adult (AYA) solid organ transplant (SOT) recipients. Participants included international experts in transplantation, behavioral psychology, patient/parent advocacy, and technology. The objectives of the symposia were as follows: (1) to identify and discuss the barriers to and facilitators of effective transfer of care for AYA SOT recipients; (2) to actively explore strategies and digital solutions to promote their successful transfer of care; and (3) to develop meaningful partnerships for the successful development, evaluation, implementation, and dissemination of these digital solutions. Additionally, data were collected from 152 AYA SOT recipients demonstrating a substantial gap in how this population uses technologies for health-related activities, alongside an increased interest in an app to help them manage their transplant.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

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

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 head0.036
GPT teacher head0.357
Teacher spread0.321 · 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
Published2019
Admission routes1
Has abstractyes

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