The Last 48 Hours of Life in Long‐Term Care: A Focused Chart Audit
Bibliographic record
Abstract
OBJECTIVES: As a component of palliative care educational program development, the faculty at the University of Ottawa Institute of Palliative Care wished to assess end-of-life care for patients in long-term care (LTC) settings to develop an educational strategy for physicians. DESIGN: A chart audit, focusing on the last 48 hours of life of residents dying in LTC facilities. SETTING: Five LTC facilities in a city in Canada. PARTICIPANTS: Residents who died in the LTC facilities in a 12-month period. Those who died suddenly (i.e., with no palliation period) or in a hospital were excluded. MEASUREMENTS: Symptoms highlighted in the literature as commonly found in the terminally ill and the matching treatments were recorded on an audit form created by the authors. Included were pain, dyspnea, noisy breathing, delirium, dysphagia, fever, and myoclonus. RESULTS: One hundred eighty-five charts were reviewed. A large number of patients were cognitively impaired. Cancer was the final diagnosis in 14% of cases. Respiratory symptoms were the most prevalent symptom, with dyspnea being first and noisy breathing third. Pain was second, with a prevalence similar to that found in studies of cancer patients. Dyspnea was not treated in 23% of the patients with this symptom; opioids were used in only 27% of cases with dyspnea. Ninety-nine percent of patients who experienced pain were treated for it. Less than one-third of patients with noisy breathing were treated. Delirium was not treated in 38% of the cases, and no anti-dopaminergic medications were administered. Nurses were primarily responsible for documenting end-of-life issues, supporting the families of the dying residents, and communicating with other team members. CONCLUSION: The focused chart audit identified the high prevalence of cognitive impairment in the patient population, which complicates symptom management. Respiratory symptoms predominated in the last 48 hours of life. This symptom profile differs from that of cancer patients, who, according to the literature, have more pain and less respiratory trouble. Management of symptoms was variable. Nurses played a crucial role in the care of dying residents through their documentation and communication of end-of-life issues. Appropriate palliative care education can provide knowledge and skills to all health-care professionals, including physicians, and assist them in the control of symptoms and improvement of quality of life for patients dying in LTC facilities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".