Longitudinal patterns of delirium severity scores in long-term care settings
Bibliographic record
Abstract
BACKGROUND: The delirium index (DI) is a valid measure of delirium severity. We proposed to describe longitudinal patterns of severity scores in older long-term care (LTC) residents. METHODS: A prospective cohort study of 280 residents in seven LTC facilities in Montreal and Quebec City, Canada, was conducted. DI, Barthel Index, Mini-Mental State Examination, Charlson Comorbidity Index, Cornell Scale for Depression in Dementia, dementia assessment by an MD, and prevalent or incident probable delirium defined according to the Confusion Assessment Method were completed at baseline. The DI was also assessed weekly for 6 months. Demographic characteristics were abstracted from resident charts. Cluster analysis for longitudinal data was used to describe longitudinal patterns of DI scores. RESULTS: During the 24 weeks following enrolment, 28 (10.0%) of 280 residents who had prevalent delirium and 76 (27.1%) who had incident delirium were included in our analysis. Average observation period was 18.3 weeks. Four basic types of time evolution patterns were discovered: Improvement, Worsening, Fluctuating, and Steady, including 22%, 18%, 25%, and 35%, of the residents, respectively. With the exception of the Worsening pattern, the average trajectory was stabilized at the 4th week or earlier. Poor baseline cognitive and physical function and greater severity of delirium predicted worse trajectories over 24 weeks. CONCLUSIONS: The longitudinal patterns of DI scores found in LTC residents resemble those found in an earlier study of delirium in acute care (AC) settings. However, compared to AC patients, LTC residents have a smaller DI variability over time, a less frequent Improvement pattern, and more frequent Worsening and Fluctuating patterns.
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 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 | 0.002 |
| 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".