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Record W3007922370 · doi:10.1093/jcag/gwz047.064

A65 VARIATION IN THE CARE OF CHILDREN WITH INFLAMMATORY BOWEL DISEASE: A CANGIEC POPULATION-BASED STUDY

2020· article· en· W3007922370 on OpenAlexaffabout
M Ellen Kuenzig, Harminder Singh, Alain Bitton, Gilaad G. Kaplan, Matthew Carroll, Anthony Otley, Thérèse A. Stukel, Sarah Spruin, Anne M. Griffiths, David R. Mack, Kevan Jacobson, Geoffrey C. Nguyen, Laura E. Targownik, Soheila Nasiri, Eric I. Benchimol

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsMount Sinai HospitalHospital for Sick ChildrenUniversity of AlbertaChildren's Hospital of Eastern OntarioMcGill UniversityUniversity of CalgaryUniversity of TorontoBC Children's HospitalDalhousie UniversityRoyal Victoria HospitalUniversity of Manitoba
Fundersnot available
KeywordsMedicineInflammatory bowel diseaseHazard ratioPediatricsHealth careDiseasePopulationEmergency medicineInternal medicineConfidence intervalEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Background Inflammatory bowel disease (IBD) is rising rapidly in Canadian children. These children require consistent high-quality specialized care to prevent long-term complications. Aims Evaluate variation in health services utilization and surgery rates across pediatric IBD centres in Ontario. Methods Incident cases of IBD <16y (1999–2010), identified from health administrative data using a validated algorithm, were assigned to pediatric IBD centres based on location of IBD hospitalization, endoscopy and outpatient care. Children receiving IBD-specific care outside pediatric centres were also grouped. Frailty models, median hazard ratios (MHR), and Kendall’s t described variation in IBD-related ED visits, hospitalizations, and surgery 6–60 months after diagnosis, adjusting for age, sex, rural/urban household, and income. Mean diagnostic lag (time from first health system contact for an IBD symptom to final IBD diagnosis) and proportion of children with IBD care by gastroenterologists (GIs) at each centre were evaluated as centre-level predictors of variation. Results Of 2584 IBD cases, 73.4% were treated in a pediatric IBD centre. Between-centre differences accounted for 0.18% (MHR 1.06) and 0.41% (MHR 1.09) of variation in hospitalizations and ED visits, respectively. Children treated at centres where a higher proportion of children were cared for by GIs were more likely to be hospitalized (HR 2.09, 95% CI 1.26–3.45). Children treated at centres with a longer mean diagnostic lag were also more likely to be hospitalized (HR 1.01, 95% CI 1.003–1.02). ED visits were not associated with the proportion of children cared for by gastroenterologists or diagnostic lag. Among 1529 CD cases, 14.1% required intestinal resection; 1.79% of variation in the risk of surgery resulted from between-centre differences (MHR 1.20). Surgery was less common among patients at centres where more children were cared for by GIs (HR 0.24, 95% CI 0.07–0.84) and with a longer mean diagnostic lag (HR 0.98, 95% CI 0.97–0.99). After adjusting for these, between-centre differences accounted for 0.005% (MHR 1.01) of variation in care. Minimal variation was observed among the 11.0% of 872 UC cases requiring colectomy, with 0.37% of variation due to between-centre differences (MOR 1.09). Colectomy risk was not associated with GI care or diagnostic lag. Conclusions Variation in ED visits, hospitalizations, and surgery among children with IBD is small; however, centre-level differences in GI specialist care use and time to diagnosis were associated with hospitalization and surgery. It is essential to understand between-centre differences to reduce variation and ensure high-quality care. Funding Agencies CCC

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)0.001
metaresearch head score (Gemma)0.004
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 score0.149
Threshold uncertainty score0.299

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.005
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.001
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 head0.004
GPT teacher head0.195
Teacher spread0.191 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the Canadian Association of Gastroenterology→Same topicInflammatory Bowel Disease→French-language works237,207→