Change in medical and health care decision-making patterns at the End-of-Life: A cohort of the very old people
Bibliographic record
Abstract
Abstract How does medical and healthcare decision-making among the very old people change in their last year before death? We explored patterns of decision-making in the Health ABC cohort study in 2011-14 (years 15-17), which involved 12 waves of quarterly phone interviews. When the participant was unable to do the interview, a proxy completed it instead. We identified a sample of 291 decedents (aged 90.0±2.9 at death, 35.7% Black, 52.6% female) with at least 1-year follow-up before death. Percentages of decedents who have made medical or healthcare decisions in the last four quarters before death were 32.0%, 31.2%, 32.6%, 41.9%, respectively. Decedents made more healthcare decisions in the last quarter before death (P<0.01), compared to the baseline. Across all quarters, decision-making is most in need for medications (17.6%), hospital admission (13.2%), and ER/urgent care visit (13.2%). We matched a 1:1 sample of survivors at the time of death by race, sex, and age (within ±3 years). In random effects models with multiple imputations of missing data, we found that decedents were more likely to make healthcare decisions than survivors across all four quarters before death or censor (Odds ratio=1.99, 95%CI: 1.49-2.65, P<0.01). The significance still held after adjusting for age, female, race, education, and interview methods. Overall, compared to matched survivors, the frequency of making medical and healthcare decisions among the very old decedents has already been high in the last year before death. This frequency rose sharply in the last quarter before death.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".