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Record W4406503228 · doi:10.1136/bmjpo-2025-gosh.105

194 Machine learning for early prediction of sepsis from electronic health records: a preliminary validation study at GOSH

2025· article· en· W4406503228 on OpenAlexaff
Stella Champeaux, Stuart A Bowyer, John Booth, Daniel Key, Neil J. Sebire

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMachine Learning in Healthcare
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsSepsisHealth recordsComputer scienceMedicineHealth careInternal medicine

Abstract

fetched live from OpenAlex

Anticoagulation in paediatric congenital heart disease; the use of RivaroxabanThrombosis is one of the most frequent complications affecting children with congenital heart disease, leading to increased mortality and morbidity.Anticoagulation with antiplatelet(s) and/or thrombolytic agents are used for different prophylactic or treatment indications.In 2020 a randomized EINSTEIN-Jr study showed similar efficacy and safety for rivaroxaban as standard anticoagulation for treatment of paediatric venous thromboembolism (VTE).The rivaroxaban dosing strategy was established based on phase 1 and 2 data in children and through pharmacokinetic (PK) modelling.Rivaroxaban is a direct inhibitor of activated factor X and known as a direct-acting oral anticoagulant (DOAC).No routine blood test is needed.Still, there are monitoring requirements for bleeding, bruising or anaemia.Liver function, urea and electrolytes, full blood count and clotting screen should be performed before treatment start.The patient should have an urgent clinical review if prolonged bleeding, head injury, unexplained bruising, or unexplained behaviour.In 2020, following approval by the committee for drugs and therapeutics Great Ormond Street Hospital (GOSH) released the use of Rivaroxaban for treatment and Thromboprophylaxis of VTE in patients 0-18 years.The overall governance was managed by the haematology team.Data was collected for six months following GOSH implementation of Rivaroxaban and again after the drug was included in the BNFc.The first findings of the audit were as follows:. Using Rivaroxaban at GOSH had initial challenges.It had been used and offered in the clinical setting but then the drug became unavailable. .Local General Practitioners were unable to provide prescriptions for the treatment period. .Education standards were created to ensure safety in the community. .Monograph insertion into the BNFc, Rivaroxaban became more widely accepted by all professional groups including those outside paediatric cardiology. .Rivaroxaban seems to be used for other conditions, not only VTE.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.519
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

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

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 head0.019
GPT teacher head0.307
Teacher spread0.287 · 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 teacher head, 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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