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Record W2795005200 · doi:10.1097/sga.0000000000000345

Healthcare Transition in Pediatrics and Young Adults With Inflammatory Bowel Disease

2018· article· en· W2795005200 on OpenAlexaff
Noelle Rohatinsky, Tracie Risling, Maha Kumaran, Laurie-ann M. Hellsten, Nancy Thorp-Froslie

Bibliographic record

VenueGastroenterology Nursing · 2018
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsInflammatory bowel diseaseMedicineDiseaseHealth careIncidence (geometry)Young adultMEDLINEAdult carePediatricsQuality of life (healthcare)Intensive care medicineTransitional careTransition (genetics)Family medicineGerontologyNursingInternal medicine

Abstract

fetched live from OpenAlex

The incidence of inflammatory bowel disease has steadily increased in children within the last decade. As young adults transition into the adult healthcare system, lack of support can lead to disease exacerbations and disease-related complications. The purpose of this scoping review was to examine the current healthcare transition literature in pediatrics and young adults with inflammatory bowel disease, with a particular focus on assessment or screening tools to evaluate healthcare transition readiness. Five most relevant databases were searched. Of these, 22 articles met the inclusion criteria and key findings from these are summarized. The majority of articles focused on adolescents or young adults with inflammatory bowel disease and were primarily published in the United States. Since 2008, there has been a growing trend in publications of inflammatory bowel disease healthcare transition literature. Articles were often described as healthcare transition readiness assessment tools, patient outcomes following transition, or transition experiences and barriers. An understanding of the current literature on the readiness assessment and support strategies is required to promote an improved quality of life for pediatric and young adult patients living with inflammatory bowel disease.

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 score0.012
Threshold uncertainty score0.717

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 integrity0.0000.001
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 head0.017
GPT teacher head0.332
Teacher spread0.315 · 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

Citations12
Published2018
Admission routes1
Has abstractyes

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