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Record W4414963988 · doi:10.1136/gutjnl-2025-basl.2

O2 Implementing a nurse-led early discharge clinic for patients with decompensated chronic liver disease to reduce readmission rates and length of hospital stay

2025· article· en· W4414963988 on OpenAlexaboutno aff
Iona Coltart, Christopher Wong, Valerie Duckhouse, Alfredo Lim, Kuldeep Cheent

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsnot available
Fundersnot available
KeywordsCohortLiver transplantationRetrospective cohort studyCohort studyLiver diseaseHealth carePopulationMortality rate

Abstract

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Following a patient’s initial presentation with decompensated liver disease, the condition often progresses rapidly, ultimately resulting in death or liver transplantation (Taper, et al.,2016). In-hospital mortality for this population ranges from 10–20% (Volk et al.,2012), and frequent unplanned readmissions pose significant burden on patients and healthcare services. Most readmissions can be avoided with effective secondary preventative measures; however, patients are frequently readmitted prior to outpatients’ follow-up (Giles, et al.,2023). This project aims to implement a nurse-led Early Discharge Clinic (EDC) for patients with decompensated cirrhosis, intending to optimise patients‘ follow-up, subsequently reducing the waiting list for outpatient clinics, reducing the delay in implementing early interventions, reducing readmission rates and, where possible, the length of hospital stays. This clinic intends also to improve overall patient and families’ experiences, and ultimately reduce costs for the NHS. A first retrospective data analysis was conducted, looking at patients with decompensated cirrhosis admitted to hospital from September to December 2023 (cohort1). Patients’ length of admission, date of first appointment offered, date of first appointment and rate of readmissions between discharge and first outpatient appointment was evaluated. A further quantitative data analysis was conducted after implementing the EDC from February to August 2024 (cohort2). A patient and family feedback form was completed to include qualitative data. Following the implementation of the EDC, patients in cohort 2 experienced significantly shorter hospital stays (8days vs. 15days in cohort 1) and substantially reduced waiting times for outpatient follow-up, with cohort 1 facing delays nearly three times longer. Patients in cohort 2 demonstrated a lower readmission rate. While direct measurement of morbidity and mortality was not feasible in this project, existing evidence supports the association between early intervention and improved clinical outcomes in decompensated liver disease. Notably, 90% of patients and families (cohort2) rated the care received as ‘very good’ or’excellent.’ The implementation of a nurse-led EDC has demonstrated clear value in improving the care of patients with decompensated cirrhosis. While the findings are encouraging, it is important to acknowledge the small sample size. This project remains ongoing and continues to evolve, with the establishment of a direct partnership with dietitians and physiotherapists aiming to further enhance the quality and scope of holistic care provided. Additionally, a notable outcome of the project has been the increased job satisfaction reported by the Specialist Nurses leading the clinic, reinforcing the positive impact of such projects on staff engagement and professional fulfilment. 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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)0.003
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.008
GPT teacher head0.302
Teacher spread0.294 · 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
Published2025
Admission routes1
Has abstractyes

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