Enhancing Communication in End‐of‐Life Care: A Clinical Tool Translating Between the Clinical Frailty Scale and the Palliative Performance Scale
Bibliographic record
Abstract
OBJECTIVES: To create a clinical tool to translate between the Clinical Frailty Scale (CFS), which geriatrics teams use, and Palliative Performance Scale (PPS), which palliative care teams use, to create a common language and help improve communication between geriatric and palliative care teams. DESIGN: Cross-sectional. SETTINGS: Two academic health centers: inpatient palliative care and chronic care units, an outpatient geriatric clinic, and inpatient referrals to a palliative care consultation service. PARTICIPANTS: Older adults (≥65) aged 80.9±8.0, with malignant (51%) and nonmalignant (49%) terminal diagnoses (N=120). MEASUREMENTS: Each participant was assigned four scores: a CFS score each from a geriatric physician and nurse and a PPS score each from a palliative care physician and nurse. Interrater reliability of each measure was calculated using kappa coefficients. For each measure, the mean of physician and nurse scores was used to calculate every possible combination of CFS and PPS scores to determine the combination with maximum agreement. RESULTS: Interrater reliability of each measure was very high for the CFS (weighted κ=0.92) and PPS (weighted κ=0.80). The CFS-PPS score matching that achieved maximum agreement (weighted κ=0.71) was used to create a conversion chart between the two measures. CONCLUSION: This conversion chart is a reliable means of translating scores between the CFS and PPS and is useful for geriatric and palliative care teams collaborating in the care of elderly adults.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".