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Record W4413309124 · doi:10.1136/bmjopen-2025-099524

Ward AdmiSsion of Haematuria: an Observational mUlticentre sTudy (WASHOUT) – study protocol

2025· article· en· W4413309124 on OpenAlexaff
Nikita Bhatt, Kevin G. Byrnes, Simona Ippoliti, Raghav Varma, Bing Jie Chow, Quentin Mak, Nikki Kerdegari, Aqua Asif, Arjun Nathan, Alexander Ng, John McGrath, Ben Lamb, James W.F. Catto, Ben Challacombe, María J. Ribal, Graeme MacLennan, Kevin Gallagher, Sinan Khadhouri, Veeru Kasivisvanathan

Bibliographic record

VenueBMJ Open · 2025
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsSt. Thomas Hospital
FundersUrology FoundationRosetrees Trust
KeywordsMedicineObservational studyWorkforceEmergency medicineProspective cohort studyIntensive care medicineSurgeryInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Haematuria contributes significantly to emergency urology admissions with over 4 per 1000 annual UK emergency admissions and 10% readmitted within 30 days. However, there is limited focus on optimising inpatient pathways internationally. Existing studies highlight a substantial underlying malignancy rate (32%) in patients presenting with visible haematuria, yet many receive inconsistent care, leading to prolonged hospital stays and increased resource use. A systematic review performed by our research group found no large-scale prospective studies have been performed in this area, and little is known about current practice. This study aims to address these gaps by investigating current management practices and their impact on outcomes, with the goal of informing evidence-based guidelines and improving patient care. METHODS AND ANALYSIS: The Ward AdmiSsion of Haematuria: an Observational mUlticentre sTudy is an international, multicentre prospective observational study designed to describe the management of patients with unplanned admission to hospital with haematuria under the care of the urology team. The study will use a collaborative methodology using the British Urology Researchers in Surgical Training model. This model delivers international multicentre studies by empowering trainees to lead all aspects of multi-centre clinical studies, building research skills cost-effectively while shaping the future urological consultant workforce. Data on demographics, comorbidities, management practices and outcomes will be collected using a standardised case report form and analysed using multilevel linear regression modelling. Primary outcomes include length of stay, while secondary outcomes cover hospitalisation free survival, mortality, readmission rates at 90 days and resource use. The study was launched in January 2024 and will continue follow-up data collection through December 2025. Patient and public involvement (PPI) has been integral to the study design, ensuring that outcomes reflect patient priorities and that the research addresses key areas of concern. ETHICS AND DISSEMINATION: Ethical and regulatory approvals will be obtained as required in each participating region. In the UK, the study is classified as a service evaluation and does not require individual patient consent. Participating sites must obtain local audit department approval. Data will be collected and stored securely, ensuring patient confidentiality. Results will be disseminated through scientific conferences, peer-reviewed publications and patient advocacy groups.

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)0.018
metaresearch head score (Gemma)0.016
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.021
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.016
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0030.002
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0210.007

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 head0.231
GPT teacher head0.533
Teacher spread0.302 · 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
GenreProtocol

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
Published2025
Admission routes1
Has abstractyes

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