Advance Directives Change Frequently in Nursing Home Residents
Bibliographic record
Abstract
OBJECTIVES: To describe the rate, timing, and pattern of changes in advance directives (ADs) of do not resuscitate (DNR) and do not hospitalize (DNH) orders among new admissions to nursing homes (NHs). DESIGN: A retrospective cohort study. SETTING AND PARTICIPANTS: Admissions to all publicly funded NHs in Ontario, Canada, between January 1, 2013, and December 31, 2017. METHODS: Residents were followed until discharged from incident NH stay, death, or were still present at the end of study (December 31, 2019). They were categorized into 3 mutually exclusive baseline composite AD groups: Full Code, DNR Only, and DNR+DNH. We used Poisson regression models to estimate the incidence rate ratios of AD change between different AD groups and different decision makers for personal care, adjusted for baseline clinical and sociodemographic variables. RESULTS: A total of 102,541 NH residents were eligible for inclusion. Residents with at least 1 AD change accounted for 46% of Full Code, 30% of DNR Only, and 25% of DNR+DNH group. Median time to first AD change ranged between 26 and 55 weeks. For Full Code and DNR Only residents, the most frequent change was to an AD 1 level lower in aggressiveness or intervention, whereas for DNR+DNH residents the most frequent change was to DNR Only. About 16% of residents had 2 or more AD changes during their stay. After controlling for covariates, residents with a DNR-only order or DNR+DNH orders at admission and those with a surrogate decision maker were associated with lower AD change rates. CONCLUSIONS AND IMPLICATIONS: Measuring AD adherence rates that are documented only at a particular time often underestimates the dynamics of AD changes during a resident's stay and results in an inaccurate measure of the effectiveness of AD on resident care. There should be more frequent reviews of ADs as they are quite dynamic. Mandatory review after an acute change in a resident's health would ensure that ADs are current.
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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 | 0.003 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".