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Record W4323351088 · doi:10.1093/jcag/gwac036.215

A215 EVALUATING THE COMPARABILITY OF CARE FOR PERSONS ADMITTED TO TORONTO AREA HOSPITALS WITH ACUTE SEVERE ULCERATIVE COLITIS

2023· article· en· W4323351088 on OpenAlexaffabout
Sonya Vukovic, Xiaobing You, S Roberts, Fahad Razak, Amol A. Verma, Laura E. Targownik

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsMount Sinai HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineExacerbationGuidelineUlcerative colitisEmergency medicineSpecialtyIntensive care medicineInternal medicineFamily medicineDisease

Abstract

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Abstract Background Approximately 20% of patients with ulcerative colitis will experience an acute severe exacerbation requiring hospitalization. Acute severe ulcerative colitis (ASUC) is a medical emergency associated with significant morbidity and a mortality rate of 1%. Timely initiation of treatment and assessment of clinical response is critical in the management of ASUC. With an aim to reduce treatment variability and improve outcomes, multiple gastrointestinal societies have published guidelines highlighting recommendations for optimal care in ASUC. It remains unclear how closely these guidelines are implemented in clinical practice. Measuring adherence to these recommended processes of care may act as a surrogate measure for quality of care and a way to indirectly evaluate outcomes in the management of patients with ASUC. Studies have shown that even amongst experienced providers practice pattern variability exists. Identifying significant variations in the management of patients with ASUC will highlight where improvement in guideline dissemination and greater adherence is required. Purpose We sought to evaluate how quality of care indicators varied across 7 hospital sites for patients admitted ASUC in the Greater Toronto Area. Method Using GEMINI, a research collaborative that collects and analyses data from inpatient admissions at 7 Toronto area hospitals, we identified patients admitted to hospital with ASUC from June 2016-December 2019. Hospital sites were further categorized into 3 hospital types; 1 IBD specialty centre (ISC), 3 other academic centres (AC) and 3 community centres (CC). Process measures assessed included proportion tested for C-reactive protein at baseline and following treatment initiation, duration of corticosteroid use, timing and initiation of biologic agents, rates of venous thromboembolism prophylaxis and opioid use. Outcome measures included hospital length of stay, rates of colectomy and mortality. Result(s) 765 hospitalizations were included in the study; 320 occurring at ISC, 308 at AC and 137 at CC. Corticosteroid use on admission were highest at the ISC at 78% compared to 64% at AC and 63% at CC (p <0.001). Among those who received steroids on admission, 47% of patients remained on intravenous corticosteroids for at least 5 days in the ISC compared to 39% in AC and 75% in CC (p< 0.001). Initiation of biologic rescue therapy was highest at the ISC occurring in 37% of hospitalizations compared to 22% in AC and 23% in CC (p<0.001). In addition, VTE prophylaxis rates were highest at the ISC at 83% followed by 60% in AC and 45% in CC (p<0.001). Rates of colectomy were highest at ISC (12% of hospitalizations vs. 7% in AC). Conclusion(s) Greater adherence to indicators of quality of care were seen at the ISC compared to ACs and CCs, although patient outcomes assessed were not clearly different between sites. Further strategies are required to improve adherence to markers of quality care for patients admitted with ASUC. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared

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)0.004
metaresearch head score (Gemma)0.019
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 score0.546
Threshold uncertainty score0.903

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.000
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 head0.039
GPT teacher head0.390
Teacher spread0.351 · 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
Published2023
Admission routes2
Has abstractyes

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