IDENTIFYING AND PREDICTING PLACE OF CARE TRAJECTORIES DURING THE LAST THREE YEARS OF LIFE
Bibliographic record
Abstract
Abstract Aging in place is a goal for most older adults, however in presence of advanced illness and/or Alzheimer’s disease and related dementia (ADRD) institutional placement may be needed. The number of days in each care setting in each quarter of the last three years of life was determined through linkage of several Medicare datasets. Using group-based modeling we examined place of care trajectories during the last three years of life among a 10% sample of Medicare beneficiaries who died in 2018 (n=199,828), providing a pre-COVID19 baseline for future studies, to identify dual trajectories of inpatient/institutional care, and skilled home healthcare/home hospice. Nine distinct trajectory classes and their associated sociodemographic and clinical characteristics were identified. The first three classes were characterized by healthcare use concentrated in the last three months of life (comprising 29.1%, 20.5%, and 9.1% of the sample); the next three classes were characterized by low use of inpatient/institutional care with long-term use of skilled home healthcare/hospice (14.2%, 8.2%, and 4.8%); and the remaining inpatient/institutional classes characterized by low use of skilled home healthcare/hospice with long-term use of inpatient/institutional care (4.8%, 4.9%, and 4.6%). The beneficiaries in the remaining sample are assigned to a place of care trajectory class using the parameter estimates derived from the 10% sample without further model fitting. We investigate the prediction utility of risk factors in multiple domains of demographic, clinical, and social determinants of health. Our findings may help to inform care preference during last period of life understanding the associated factors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".