The prevalence of delirium in Belgian nursing homes: a cross-sectional evaluation
Bibliographic record
Abstract
BACKGROUND: Delirium is a common geriatric syndrome, but only few studies have been done in nursing home residents. Therefore, the aim of this study was to investigate (point) prevalence of and risk factors for delirium in nursing homes in Belgium. METHODS: A multisite, cross-sectional study was conducted in six nursing homes in Belgium. Residents of six nursing homes were screened for delirium. Exclusion criteria were coma,'end-of-life' status and residing in a dementia ward. Delirium was assessed using the Delirium Observation Screening Scale. RESULTS: 338 of the 448 eligible residents were included in this study. Of the 338 residents who were evaluated, 14.2 % (95 %CI:3.94-4.81) screened positive for delirium with the Delirium Observation Screening Scale. The mean age was 84.7 years and 67.5 % were female. Taking antipsychotics (p = 0.009), having dementia (p = 0.005), pneumonia (p = 0.047) or Parkinson's disease (p = 0.03) were more present in residents with delirium. The residents were more frequently physically restrained (p = 0.001), participated less in activities (p = 0.04), had had more often a fall incident (p = 0.007), had lower levels of cognition (p < 0.001; MoCA ≥ 26, p = 0.04; MoCA ≥ 25, p = 0.008) and a higher "Activities of Daily Living" score (p = 0.001). In multivariable binary logistic regression analysis, a fall incident (2.76; 95 %CI: 1.24-6.14) and cognitive impairment (OR: 0.69; 95 %CI: 0.63-0.77) were significantly associated with delirium. CONCLUSIONS: Delirium is an important clinical problem affecting almost 15 % of the nursing home residents at a given moment. Screening of nursing home residents for risk factors and presence of delirium is important to prevent delirium if possible and to treat underlying causes when present.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".