Development and validation of clinical profiles of patients hospitalized due to behavioral and psychological symptoms of dementia
Bibliographic record
Abstract
BACKGROUND: Patients hospitalized on acute psychogeriatric wards are a heterogeneous population. Cluster analysis is a useful statistical method for partitioning a sample of patients into well separated groups of patients who present common characteristics. Several patient profile studies exist, but they are not adapted to acutely hospitalized psychogeriatric patients with cognitive impairment. The present study aims to partition patients hospitalized due to behavioral and psychological symptoms of dementia into profiles based on a global evaluation of mental health using cluster analysis. METHODS: Using nine of the 13 items from the Health of the Nation Outcome Scales for elderly people (HoNOS65+), data were collected from a sample of 542 inpatients with dementia who were hospitalized between 2011 and 2014 in acute psychogeriatric wards of a Swiss university hospital. An optimal clustering solution was generated to represent various profiles, by using a mixed approach combining hierarchical and non-hierarchical (k-means) cluster analyses associated with a split-sample cross-validation. The quality of the clustering solution was evaluated based on a cross-validation, on a k-means method with 100 random initial seeds, on validation indexes, and on clinical interpretation. RESULTS: The final solution consisted of four clinically distinct and homogeneous profiles labeled (1) BPSD-affective, (2) BPSD-functional, (3) BPSD-somatic and (4) BPSD-psychotic according to their predominant clinical features. The four profiles differed in cognitive status, length of hospital stay, and legal admission status. CONCLUSION: In the present study, clustering methods allowed us to identify four profiles with distinctive characteristics. This clustering solution may be developed into a classification system that may allow clinicians to differentiate patient needs in order to promptly identify tailored interventions and promote better allocation of available resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".