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Record W4416586874 · doi:10.1016/j.eclinm.2025.103641

Prevalence of cognitive morbidity including delirium in 51,202 emergency hospital admissions across 29 medical and surgical specialties in ORCHARD-EPR: a cross-sectional study

2025· article· en· W4416586874 on OpenAlexfundaboutno aff
Emily Boucher, S C L Smith, Sudhir Singh, Sasha Shepperd, Sarah T. Pendlebury

Bibliographic record

VenueEClinicalMedicine · 2025
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsnot available
FundersInvention for InnovationNIHR Oxford Biomedical Research CentreRhodes ScholarshipsNational Institute for Health and Care ResearchCanadian Institutes of Health ResearchNuffield Department of Population Health, University of Oxford
KeywordsDeliriumCognitionMEDLINEHospital admissionCognitive impairmentEmergency medical services

Abstract

fetched live from OpenAlex

Background: Older people account for a growing proportion of unplanned hospital admissions and many have complex conditions. However, there are few data on cognitive morbidity (delirium, dementia and low cognitive test score) by specialty to plan services and guide policy. We therefore determined the occurrence of cognitive morbidity hospital-wide in older hospital patients using electronic patient record (EPR) data. Methods: The Oxford and Reading Cognitive Comorbidity, Frailty and Ageing Research Database (ORCHARD-EPR) includes data on consecutive patients aged ≥70 years with length of stay of ≥1 day (1st January 2017-31st December 2019) admitted to four hospitals covering Oxfordshire, UK. ORCHARD-EPR includes information from a mandatory on-admission cognitive screen comprising the 10-point Abbreviated Mental Test (AMT), dementia history and documentation of delirium where delirium diagnosis is based on a holistic assessment incorporating the AMT, Confusion Assessment Method (CAM) and clinical notes. Delirium and dementia diagnosis from the cognitive screen was supplemented by discharge ICD-10 coding. Prevalence of cognitive morbidity was determined hospital-wide and then by specialty. Findings: Among 51,202 admissions (mean/SD age = 82/7 years), any cognitive morbidity was present in 18,225 (35.6%, 95% CI 35.2-36.0%): delirium occurred in 24.0% (n = 12,289, of which 14.3% (n = 7332) had delirium only and 9.7% (n = 4957) had delirium + dementia) dementia only in 8.7%, (n = 4450), AMTS <8 in 2.9% (n = 1486). The prevalence of cognitive morbidity was highest in geriatrics (44.5%; n = 134/301), general medicine (42.8%; n = 14,346/33,512), trauma/orthopaedics (36.4%; n = 1337/3673), palliative care (36.0%; n = 128/356), stroke (30.8%; n = 144/468), infectious disease (27.6%; n = 42/152), neurosurgery (22.9%; n = 161/702) and general surgery (21.5%; n = 822/3819) and was 10-20% in all other specialties except two. Delirium was the most prevalent cognitive morbidity subtype in 24/29 specialties. Interpretation: Cognitive morbidity was common in older people with unplanned hospital admission across a broad range of specialties, with delirium accounting for most cases. Findings support the need for hospital-wide delirium screening and access to multidisciplinary team input for all specialties. Funding: Rhodes Trust, Canadian Institutes of Health Research, National Institutes for Health Research.

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)0.003
metaresearch head score (Gemma)0.152
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.152
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.059
GPT teacher head0.453
Teacher spread0.393 · 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.

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
Published2025
Admission routes2
Has abstractyes

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