Examining the Risk of Delirium Among Residents with Diabetes in Long-Term Care Homes Across Ontario
Bibliographic record
Abstract
Aim: To examine risk factors for delirium in residents with diabetes in Ontario’s LTC homes. Scope: Residents in long-term care (LTC) are vulnerable to negative outcomes related to diabetes, including delirium. Understanding factors related to the risk of delirium for residents with diabetes provides the foundation for the mitigation of delirium in this population. Methods: A population-based retrospective analysis of the RAI-MDS dataset (2019–2020) was conducted. Findings: Diabetes was associated with a statistically significant increased risk of delirium (Odds Ratio: 1.073, CI 1.038–1.109), compounded by polypharmacy. Conclusions: Comprehensive delirium mitigation strategies are needed for this vulnerable population. Strategies to mitigate delirium in this population should be implemented.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".