Abstract 13306: Advanced Care Planning in Persons Who Die From Heart Disease
Bibliographic record
Abstract
Introduction: The unpredictable trajectory of persons dying from cardiovascular disease (CVD) may make planning for future care challenging. In order to target quality improvement (QI) efforts, it is necessary to identify factors associated with advanced care planning (ACPing) in CVD. Methods: The Health Retirement Survey (a nationally representative sample) provided data for a secondary data analysis of 1304 individuals’ ≥65 years of age that died of CVD from 2002-2012. Proxies of decedents reported ACPing activities (identifying a durable power of attorney for health care [DPOA_HC], completing a living will, or engaging in a conversation about care preferences at end of life (EoL). Demographic and clinical factors were also recorded. Results: Proxies reported 78% of decedents engaged in some level of ACPing (60% had a DPOA_HC, 56.5% had a conversation about EoL treatment preferences, and 47.3% completed a living will). In bivariate analysis, being older, female, Caucasian, widowed, dying at home/hospice/other, having higher income or education, and more symptoms were associated with any type of ACPing (chi-squared test p-value ranges between <0.000 and 0.028). In the multivariate regression model, older age (OR=1.026, 95%CI [1.002, 1.052], p=0.037), Caucasian race (OR=1.754, 95%CI [1.126, 2.735], p=0.013), more education (‘high school grad’: OR=1.723, 95%CI [1.151, 2.580], p=0.008; ‘some college’: OR=2.659, 95% CI [1.451, 4.871], p=0.002; ‘college grad’: OR=3.404, 95%CI [1.710, 6.774], p<0.000), higher income (‘$10,001-$88,000’: OR=1.670, 95%CI [1.000, 2.791], p=0.050; ‘>$88,000’: OR=2.012, 95%CI [1.150,3.520], p=0.014), and more symptom burden (OR=1.350, 95%CI [1.217, 1.498], p<0.000) were associated with any type of ACPing. Conclusions: The majority of individuals dying of CVD participate in ACPing. QI efforts to improve ACPing should target non-Caucasian individuals with less education, less income, younger age, and lower symptom burden.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".