Predictors of Advance Directive Changes in Ontario Nursing Home Residents: A Case–Control Study
Bibliographic record
Abstract
BACKGROUND: Goals of care (GOC) discussions between the clinical team and nursing home (NH) residents provide the basis for decision-making on advance directives (AD) that include do-not-resuscitate (DNR) and do-not-hospitalize (DNH). Optimal timing and prompts for initiating GOC discussions are unclear. This study investigates recent emergency department (ED) use and clinical and demographic factors associated with subsequent AD changes. METHODS: Nested case-control study within a population-based retrospective cohort using linked administrative health care data of individuals admitted to NHs in Ontario, Canada between 2013 and 2017 and then followed up until 2019. Eligible cases and controls were residents with and without an AD change between 2013 and 2019, respectively. Cases and controls were matched 1:1 by sex, composite AD at NH admission, NH admission date (±90 days), and birthdate (±365 days). The primary outcome was incident AD change, and exposures included recent ED use (either ED visits discharged back to NH or ED visits that resulted in hospitalization) and clinical and demographic variables measured at the time of documented AD change. Conditional logistic regression provided adjusted odds ratios for associations between exposures and incident AD change. RESULTS: The cases and controls (27,942 residents) had a mean age of 84 years at NH admission and 67.1% were female. 48.3% had a baseline AD of "DNR Only" while the remaining were evenly divided between "Full Code" and "DNR+DNH." The estimated adjusted odds ratio of AD change was 2.01 (95% CI, 1.83-2.21) in residents with recent hospitalization, 1.89 (95% CI, 1.67-2.13) in those having end-stage disease, and 1.82 (95% CI, 1.56-2.12) in residents who were mostly bedfast. CONCLUSIONS: A recent hospitalization, end-stage disease, or being bedfast are significant predictors of AD change. These important predictors exhibited by NH residents present opportunities to reassess GOC.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".