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Record W4247762011 · doi:10.1093/ibd/izy393.002

19 IDENTIFICATION OF PATIENTS WITH INFLAMMATORY BOWEL DISEASE AT HIGH RISK OF URGENT CARE UTILIZATION THROUGH IBD QORUS, A LEARNING HEALTHCARE SYSTEM

2019· article· en· W4247762011 on OpenAlexaboutno aff
Jason K. Hou, Anthony Xu, Brant J. Oliver, Siddharth Singh, Julie Weatherly, Julia Guardado, Alandra Weaver, Damara Crate, Corey A. Siegel, Gil Melmed

Bibliographic record

VenueInflammatory Bowel Diseases · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLogistic regressionEmergency departmentInflammatory bowel diseaseHealth careDiseaseEmergency medicineRisk assessmentCrohn's diseaseInternal medicineNursing

Abstract

fetched live from OpenAlex

The disease course of inflammatory bowel disease (IBD) is often associated with periods of response interrupted by flares. Identification of patients at high risk of utilization of urgent care resources may allow for proactive intervention and avoid unnecessary emergency department utilization, exposure to diagnostic radiography, steroid use, narcotic use, and hospitalizations. We hypothesized urgent care utilization is associated with patient factors and provider assessments. The aim of this study was to assess the association of a patient-reported tool and provider assessment with urgent care utilization. This study was performed as part of a breakthrough series collaborative of IBD Qorus, a learning healthcare system. Data were collected from patients from 30 participating Qorus [AW1] sites during routine clinical care from February 2018 to July 2018, and included provider global assessment of high-risk for urgent care utilization. Associations of patient factors and provider assessments with urgent care utilization were assessed using Chi-square test, univariate, and multivariate logistic regression. Patients were designated as “high risk” status for urgent care utilization for analyses a priori, using including age, gender, IBD type, IBD Manitoba Index, frequency of calls to clinic, and provider global assessment. Analyses were performed per IBD encounter. A total of 7,345 patient encounters were included in the study period. Among patients who met criteria for “high risk” status, 47% reported urgent care utilization in the prior 6 months, compared to only 3.5% of patients who were classified as “low risk”. Using only provider global assessment, 59% of patients assessed as “high risk” reported urgent care utilization compared to 17% of patients who were “low risk”. On univariate analyses, age, gender, IBD type, patient-reported wellbeing, Manitoba IBD Index, greater than four calls to the provider’s office in the past month, and provider global assessment, were all statistically significantly associated with use of urgent care in the preceding 6 months. On multivariate analyses, age, patient-reported wellbeing, patient calls, and provider global assessment remained significant (Table 1). The factor most associated with urgent care utilization was provider assessment (OR 3.65, 95% CI 3.13-4.27). Patient and provider assessments were associated with recent urgent care utilization among patients with IBD. The ability to identify IBD patients who are at high risk of urgent care utilization may allow providers to proactively address symptoms and potentially decrease emergency department utilization and unnecessary exposure to steroids, radiation, and narcotics. These factors will be studied prospectively as part of the IBD Qorus learning healthcare system.

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)0.003
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 score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.006
GPT teacher head0.223
Teacher spread0.217 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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