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Record W3008284277 · doi:10.1093/jcag/gwz047.065

A66 GEOGRAPHIC VARIATION AND INCREASED INVOLVEMENT OF INTERVENTIONAL RADIOLOGISTS IN THE MANAGEMENT OF REFRACTORY ASCITES: A POPULATION BASED STUDY

2020· article· en· W3008284277 on OpenAlexaffabout
Douglas Motomura, Maya Djerboua, Jennifer A. Flemming

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2020
Typearticle
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineAscitesIncidence (geometry)PopulationInternal medicineCirrhosisPoisson regressionCohortEmergency medicineEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Background The disease burden from cirrhosis is increasing worldwide. Refractory ascites (RA) is a complication of cirrhosis associated with poor prognosis if liver transplant is not an option. Serial large volume paracentesis (LVP) is the standard of care in the management of refractory ascites (RA) and outpatient LVP has been shown to be safe and cost effective. Epidemiologic data is lacking regarding the incidence of RA, or how patients with RA are managed in routine clinical practice. Aims To describe secular trends in the incidence of RA in Ontario from 2000–2017, and to describe physician provider types performing LVPs in the RA population in Ontario. Methods This retrospective, population-based cohort study uses routinely collected healthcare data from Ontario, Canada, housed at ICES. From January 1, 2000 to Dec 31, 2017 all adult patients with cirrhosis were identified using a validated case definition, and those with RA were identified based on the need for serial LVP. All LVP procedures were described based on patient demographics, local health integration network (LHIN), physician type (Gastroenterology [GI], Internal Medicine [IM], Interventional Radiology [IR], Emergency Medicine [ER], other) and albumin administration. Annual incidence rates (IR) of RA in patients with cirrhosis were calculated and compared using Poisson regression to calculate incident rate ratios (IRRs). Annual LVP volume by provider type and LHIN were calculated and differences were compared using chi-squared analysis. Results The overall incidence of RA in patients with cirrhosis remained relatively stable over the study period (IRR 1.01, 95% CI 1.00–1.02 P<.001). The highest incidence of RA was in those with viral hepatitis and alcohol-related cirrhosis. A total of 90,126 LVPs were identified (median age 61 years [IQR 53–70], 69% male, median LVP per patient 24 [IQR 11–48], 15.8% received albumin infusion). The absolute numbers of LVPs more than tripled over the study period (12,047 in 1997–2002 vs. 37,437 in 2013–2017). GI performed the majority of LVPs (40.1%) followed by IR (22.4%), and IM (8.4%), but there was substantial variation based on location (Fig 1). Overall, the proportion performed by IR increased during the study (7.8% in 1997–2002 vs 30.8% in 2013–2017, P <.001) while the proportion performed by GI decreased (50% 1997–2002 vs 33.1% 2013–2017, P<.001). Conclusions The number of LVPs performed for RA have increased dramatically in Ontario over the past two decades, with the proportion being performed by GI physicians decreasing, while IR is increasing. Substantial variability exists across LHINs on the use of LVP, which may reflect differences in access to resources for LVP, or physician practice. Appropriate albumin use with LVP remains an area for potential quality improvement initiatives in the future. Funding Agencies AASLD Foundation Clinical Translational and Outcomes Research Award in Liver Disease (for supervisor JF)

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)0.002
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 score0.620
Threshold uncertainty score0.764

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.015
GPT teacher head0.240
Teacher spread0.226 · 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
Published2020
Admission routes2
Has abstractyes

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