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Record W2157436908 · doi:10.5770/cgj.15.15

A Quality Assurance Study to Assess the One-Day Prevalence of Delirium in Elderly Hospitalized Patients

2012· article· en· W2157436908 on OpenAlexaffvenueabout
Carrie McAiney, Christopher Patterson, Esther Coker

Bibliographic record

VenueCanadian Geriatrics Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsHamilton Health SciencesHealth Sciences CentreMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsDeliriumMedicineConfusionHealth careEmergency medicinePsychiatryFamily medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Research indicates that 40% of hospital-acquired delirium cases may be preventable. However, despite its clinical significance, delirium often goes unrecognized or is misdiagnosed. The purpose of this study was to assess the need for delirium education in acute care hospitals in Hamilton, Ontario. METHODS: Approximately 100 health professionals were trained as delirium screeners. On 'Delirium Day', all patients ≥ 65 years of age in non-critical care areas in all acute care sites in Hamilton were identified. Those willing to take part in the prevalence study were assessed for delirium using the Standardized Mini-Mental State Examination and the Confusion Assessment Method. The Research Ethics Boards at Hamilton Health Sciences and St. Joseph's Healthcare Hamilton approved this quality assurance project. RESULTS: Of the 562 patients eligible for screening, eight were excluded and six did not have sufficient data collected to assess for delirium. Of the 548 individuals screened for delirium, 10.6% screened positive. Prevalence estimates ranged by site from 0% to 21% and type of unit from 3.8% to 16%. Recognition of delirium by nursing staff was fair; but, documentation was usually absent. CONCLUSION: While the prevalence rates were somewhat lower than in other studies, the results support the need for education among health-care providers in the prevention, identification, and management of delirium.

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)0.002
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.034
GPT teacher head0.314
Teacher spread0.280 · 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

Citations8
Published2012
Admission routes3
Has abstractyes

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