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Record W2206276078 · doi:10.5770/cgj.18.185

Risk of Pre- and Post-Operative Delirium and the Delirium Elderly At Risk (DEAR) Tool in Hip Fracture Patients

2015· article· en· W2206276078 on OpenAlexafffundvenue
Susan Freter, Michael Dunbar, Katalin Koller, Chris MacKnight, Kenneth Rockwood

Bibliographic record

VenueCanadian Geriatrics Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsCapital District Health AuthorityDalhousie University
FundersNova Scotia Health Research Foundation
KeywordsDeliriumMedicineHip fractureRisk factorPopulationProspective cohort studyGeriatricsOrthopedic surgeryCohortPhysical therapyInternal medicineIntensive care medicinePsychiatryOsteoporosis

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Delirium is common after hip fracture. Previous work has shown that a simple delirium risk factor tool, the Delirium Elderly At Risk instrument (DEAR), has a high inter-rater reliability in this population. Little research has looked at the ability of risk factor screening tools to identify patients at high risk of pre-operative delirium. This study investigates the ability of the DEAR to identify patients at high risk of pre-operative delirium, as well as reporting its performance in a post-operative validation sample. Associations between delirium risk factors and pre-operative delirium are explored. METHODS: This prospective cohort study took place on an orthopedic in-patient service at a University-affiliated tertiary care hospital. Patients aged 65 and older who were admitted for surgical repair of hip fracture (N = 283) were assessed pre-operatively for 5 delirium risk factors (cognitive impairment, sensory impairment, functional dependence, substance use, age) using the DEAR. Patients were assessed for delirium using the Mini-Mental State Examination and the Confusion Assessment Method pre-operatively and on post-operative days 1, 3 and 5. Characteristics of patients who developed delirium were compared with the characteristics of those who did not. RESULTS: Delirium was present in 58% (95% CI = 52-63%) of patients pre-operatively and 42% (95% CI = 36-48%) post-operatively. Individually, sensory impairment (χ(2) = 21.7, p = .0001), functional dependence (χ(2) = 24.1, p = .0001), cognitive impairment (χ(2) = 55.5, p = .0001) and substance use (χ(2) = 7.5, p = .007) were significantly associated with pre-operative delirium, as was wait-time for surgery (t = 3.1, p = .003) and length of stay (t = 2.8, p =.03). In multivariate modeling, the strongest association with pre-operative delirium was cognitive impairment. CONCLUSIONS: The DEAR, a simple, delirium risk factor screening tool, can be used to identify hip fracture patients at risk of both pre-operative and post-operative delirium, which may allow targeted implementation of delirium prevention strategies.

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)0.001
metaresearch head score (Gemma)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.235
Teacher spread0.228 · 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".

Quick stats

Citations44
Published2015
Admission routes3
Has abstractyes

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