Palliative and Therapeutic Harmonization: A Model for Appropriate Decision‐Making in Frail Older Adults
Bibliographic record
Abstract
Frail older adults face increasingly complex decisions regarding medical care. The Palliative and Therapeutic Harmonization (PATH) model provides a structured approach that places frailty at the forefront of medical and surgical decision-making in older adults. Preliminary data from the first 150 individuals completing the PATH program shows that the population served is frail (mean Clinical Frailty Score = 6.3), has multiple comorbidities (mean 8), and takes many medications (mean = 9). Ninety-two percent of participants were able to complete decision-making for an average of three current or projected health issues, most often (76.7%) with the help of a substitute decision-maker (SDM). Decisions to proceed with scheduled medical or surgical interventions correlated with baseline frailty level and dementia stage, with participants with a greater degree of frailty (odds ratio (OR) = 3.41, 95% confidence interval (CI) = 1.39-8.38) or more-advanced stage of dementia (OR = 1.66, 95% CI = 1.06-2.65) being more likely to choose less-aggressive treatment options. Although the PATH model is in the development stage, further evaluation is ongoing, including a qualitative analysis of the SDM experience of PATH and an assessment of the effectiveness of PATH in long-term care. The results of these studies will inform the design of a larger randomized controlled trial.
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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 | 0.029 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".