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Record W2926136611 · doi:10.1093/ageing/afz036

The Scottish Intercollegiate Guidelines Network: risk reduction and management of delirium

2019· article· en· W2926136611 on OpenAlexafffund
Daniel Davis, Samuel D. Searle, Alex Tsui

Bibliographic record

VenueAge and Ageing · 2019
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsNova Scotia Health Authority
FundersAlzheimer’s SocietyHealth Sciences Centre FoundationAlzheimer SocietyWellcome TrustDalhousie Medical Research Foundation
KeywordsDeliriumDementiaMedicineContext (archaeology)GuidelineDistressPsychiatryCognitionHealth careIntensive care medicineClinical psychologyDisease

Abstract

fetched live from OpenAlex

Clinical and research interest in delirium has been rising over the last 15 years. The Scottish Intercollegiate Guidelines Network (SIGN) publication on delirium is a state-of-the-art synthesis of the field, and the first UK guideline since 2010. There is new guidance around delirium detection, particularly in recommending the 4 'A's Test (4AT). The 4AT has the advantage of being brief, embeds and operationalises cognitive testing, and is scalable with little training. The guidelines highlight the importance of non-pharmacological management for all hospital presentations involving the spectrum of cognitive disorders (delirium, dementia but at risk of delirium, delirium superimposed on dementia). Pharmacotherapy has a minimal role, but specific indications (e.g. intractable distress) are discussed. Advances in delirium research, education and policy, have come together with steady changes in the sociocultural context in which healthcare systems look after older people with cognitive impairment. However, there remains a gap between desired and actual clinical practice, one which might be bridged by re-engaging with compassionate, patient-centred care. In this respect, these SIGN guidelines offer a key resource.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score0.208

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.017
GPT teacher head0.276
Teacher spread0.260 · 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 designOther design
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

Citations79
Published2019
Admission routes2
Has abstractyes

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