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Record W4213276511 · doi:10.1093/jcag/gwab049.064

A65 PREDICTING TRANSITION SUCCESS IN YOUNG ADULTS WITH INFLAMMATORY BOWEL DISEASE: PRELIMINARY RESULTS

2022· article· en· W4213276511 on OpenAlexaffabout
Allison Bihari, Karen J. Goodman, Eytan Wine, Karen I. Kroeker

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2022
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineInflammatory bowel diseaseLogistic regressionPoisson regressionDiseaseOddsFamily medicineOdds ratioRetrospective cohort studyYoung adultIncidence (geometry)Transitional careDisease managementHealth carePediatricsGerontologyInternal medicinePopulationEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Background Patients diagnosed with inflammatory bowel disease (IBD) in childhood present more often with extensive disease, are more likely to be admitted to hospital and are less adherent with clinic appointments. Due to these risks, a smooth, uninterrupted transition from pediatric to adult care should be a priority. We have conducted interviews with providers, patients, and parents about their opinions on indicators of successful transition. Themes of successful transition that emerged included independence in seeking care and disease management. Characterizing successful transition based on stakeholder input makes it possible to monitor its achievement and identify its determinants. Aims This study aims to: 1) describe the frequency of success indicators in transitioned patients and 2) identify predictors associated with success indicators. We hypothesize that patients with more experience in pediatric care (e.g., younger age at diagnosis or on biologics) are more likely to achieve success. Methods We conducted a retrospective medical chart review to obtain data on patients who transitioned to adult care between January, 2014 - September, 2019 at the University of Alberta. We abstracted potential predictors, including social and disease factors, at first adult appointment which had notes on pediatric history. We chose available success indicators related to two themes: independence in seeking care (e.g., attending appointments, communicating for oneself) and disease management (e.g., lab work frequency and medication adherence). We abstracted selected success indicators within a two-year period from first appointment in adult care. We used Poisson and logistic regression to estimate incidence rate ratios (IR) and odds ratios (OR) for the association of potential predictors with success indicators. Results We reviewed medical charts of 99 patients. At first adult appointment, the median age at diagnosis was 14.5 years old (IQR: 13.2 – 15.9) and 57.6% of patients were on biologic agents. Within two years, 42.4% of patients required a change to a different therapy, 22.2% had at least two instances where a parent called on their behalf, and 16.2% had notes of medication nonadherence in adult care. Regression analysis (Table 1) estimated that patients who lived > 100km from clinic had a lab work incidence rate in the first year that was two-thirds that of patients who lived closer. Strong predictors of non-adherence in adult care included chart notes on pediatric medication non-adherence (OR~12) and, inversely, taking biologics (OR=0.34). Conclusions These results identified factors that could be used to identify patients likely to have poor outcomes following transition to adult care. These are preliminary results; we plan to analyze a total of 350 medical charts. Funding Agencies None

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.277
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes2
Has abstractyes

Explore more

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