Integrating Palliative Care Assessment Tools to Enhance Understanding of Illness Trajectory in Post-Acute Care and Long-Term Care
Bibliographic record
Abstract
OBJECTIVES: To determine whether education and integration of the Gold Standard Framework Proactive Identification Guidance (GSF-PIG) and the Palliative Performance Scale (PPS) into care rounds, in post-acute care settings, can facilitate communication between the interprofessional care team to enhance understanding of illness trajectories, identifying those who would benefit from a palliative approach to care. METHODS: Interprofessional care teams received training on the GSF-PIG and PPS which were integrated into weekly care rounds and completed a post-evaluation survey. A chart review was conducted for the 40 patients and residents reviewed with the GSF-PIG and PPS. Data analysis included descriptive statistics and comparisons of characteristics between patients and residents who were grouped as positive or negative on the GFS-PIG surprise question using chi square analyzes and t-tests. RESULTS: The GSF-PIG and PPS were found to enhance communication within care teams and enhance understanding of patient and resident's illness burden. The chart review revealed that patients and residents whom the team would not be surprised if they died within 1 year were older (p = .002), had a lower PPS score (p = .002) and had more indicators of decline (p < .001) compared to patients and residents the team would be surprised if they died within the year. CONCLUSION: Training interprofessional care teams on the utilization and integration of the GSF-PIG and PPS during weekly care rounds helped increase the understanding of patient and resident illness burden and illness trajectory to identify those who may benefit from a palliative approach to care.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".