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Record W3006887117 · doi:10.1093/jcag/gwz047.063

A64 HOSPITALIZATION IN INFLAMMATORY BOWEL DISEASE: A POPULATION-BASED COMPARISON OF DEFINITIONS

2020· article· en· W3006887117 on OpenAlexaffabout
Stephanie Coward, Eric I. Benchimol, Çharles N. Bernstein, Alain Bitton, Matthew Carroll, Susan Jelinski, Jennifer Jones, M Ellen Kuenzig, Des Leddin, Sanjay K. Murthy, Geoffrey C. Nguyen, Anthony Otley, Ali Rezaie, Juan Nicolás Peña-Sánchez, Harminder Singh, J Stach, Laura E. Targownik, Joseph W. Windsor, Gilaad G. Kaplan

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversity of SaskatchewanUniversity of TorontoUniversity of CalgaryMount Sinai HospitalMcGill UniversityUniversity of OttawaAlberta Health ServicesUniversity of AlbertaDalhousie UniversityRoyal Victoria HospitalOttawa HospitalUniversity of ManitobaChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsMedicineInflammatory bowel diseaseDiagnosis codeCohortPopulationConfidence intervalDiseaseInternal medicinePediatrics

Abstract

fetched live from OpenAlex

Abstract Background Most administrative studies of hospitalization in inflammatory bowel disease (IBD) use two definitions: IBD in any diagnostic position (IBD-ANY), and IBD as the most responsible diagnostic (IBD-MRD). There is a third less commonly used definition: total hospitalization; this definition captures all hospitalizations of prevalent IBD patients and therefore it can give a more realistic picture of the burden of IBD. Aims To compare differing definitions (total, IBD-ANY, and IBD-MRD) of hospitalizations. Methods A previously defined population-based IBD prevalent cohort for Alberta (n=30,698) was used to pull all hospital admissions from the Discharge Administrative Database (DAD; 2002–2015). Three hospitalization definitions were used: i. Total (all hospitalizations of prevalent cohort independent of presence of code for IBD); ii. IBD-ANY (code for IBD [K50.x; K51.x] contained in any diagnosis field); and, iii. IBD-MRD (most responsible diagnosis was IBD). Age- and sex- standardized rates (2015 Canadian population) were calculated using the prevalent population. Log-linear regression was performed to calculate Average Annual Percentage Change (AAPC) with associated 95% confidence intervals (CI) of each type of hospitalization. We assessed the top five most common most-responsible diagnosis codes for hospitalizations that were contained in the total hospitalizations but not an IBD-ANY hospitalization. Results From 2002 to 2015, 63.5% of IBD prevalent patients in AB had ≥1 hospitalization; 44.2% had ≥1 IBD-ANY hospitalization; 28.6% had ≥1 IBD-MRD hospitalization; and, 40.6% had a hospitalization that did not contain a code for IBD. All hospitalization rates decreased significantly over time. Of the top five most common most responsible diagnosis, contained in admissions that were not IBD-ANY, three were gastroenterological: i. K52.9 (non-infective gastroenteritis); ii. A09.9 (diarrhea and gastroenteritis of presumed infectious origin); and, iii. Z43.2 (attention to ileostomy). Conclusions Total hospitalizations is an important measure to report since accounting for all hospitalizations of IBD patients is necessary in order to allocate healthcare resources appropriately. To be able to ensure these patients receive the care they need we need to be able to accurately assess the true burden of IBD. Funding Agencies CIHR

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)0.006
metaresearch head score (Gemma)0.014
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 score0.323
Threshold uncertainty score0.642

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.006
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
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 head0.009
GPT teacher head0.223
Teacher spread0.213 · 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
Published2020
Admission routes2
Has abstractyes

Explore more

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