Using the Edmonton Frail Scale to trigger palliative care referral for hospitalized patients with heart failure
Bibliographic record
Abstract
Objective: To determine whether use of the Edmonton Frail Scale (EFS) by the clinical staff on the medical cardiology unit would increase referrals to palliative care for hospitalized patients with heart failure. Design: Quality improvement project based on the Plan, Do, Check, Act framework. Setting/Local Problem: At a 45-bed cardiology unit at an urban academic medical center, frail hospitalized patients with heart failure were not consistently referred to palliative care. Patients: Patients (N=18) admitted with diagnoses of heart failure from July 16, 2016 through July 30, 2018. Intervention/Measurements: Medical cardiology staff were instructed on the on the new process and how to use the EFS. After training, patients with EFS scores greater than or equal to 10 were referred to palliative care. Results: Eighteen patients were admitted with heart failure: 22% (n=4) were referred to palliative care. Of these four, 75% (n=3) were referred because of EFS screening results. One attending physician declined to participate in this project. Overall, palliative care referrals increased from 9% (n=1) before the process change to 17% (n=3) during the two-week pilot. Conclusion: Frailty screening is an objective method with which to identify patients who may benefit from palliative care. Results from this two-week pilot demonstrate process improvement. However, long term sustainability remains questionable. The quality improvement team has committed to continue the pilot for three months. Keywords: palliative care, palliative care referral, heart failure, frailty, goals of care, patient preferences.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".