Latent class analysis of the multivariate Delirium Index in long-term care settings
Bibliographic record
Abstract
ABSTRACTBackground:A few studies examine the time evolution of delirium in long-term care (LTC) settings. In this work, we analyze the multivariate Delirium Index (DI) time evolution in LTC settings. METHODS: The multivariate DI was measured weekly for six months in seven LTC facilities, located in Montreal and Quebec City. Data were analyzed using a hidden Markov chain/latent class model (HMC/LC). RESULTS: The analysis sample included 276 LTC residents. Four ordered latent classes were identified: fairly healthy (low "disorientation" and "memory impairment," negligible other DI symptoms), moderately ill (low "inattention" and "disorientation," medium "memory impairment"), clearly sick (low "disorganized thinking" and "altered level of consciousness," medium "inattention," "disorientation," "memory impairment" and "hypoactivity"), and very sick (low "hypoactivity," medium "altered level of consciousness," high "inattention," "disorganized thinking," "disorientation" and "memory impairment"). Four course types were also identified: stable, improvement, worsening, and non-monotone. Class order was associated with increasing cognitive impairment, frequency of both prevalent/incident delirium and dementia, mortality rate, and decreasing performance in ADL. CONCLUSION: Four ordered latent classes and four course types were found in LTC residents. These results are similar to those reported previously in acute care (AC); however, the proportion of very sick residents at enrolment was larger in LTC residents than in AC patients. In clinical settings, these findings could help identify participants with a chronic clinical disorder. Our HMC/LC approach may help understand coexistent disorders, e.g. delirium and dementia.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".