A Quality Assurance Study to Assess the One-Day Prevalence of Delirium in Elderly Hospitalized Patients
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".