Pain Management in Frail Hip Fracture Patients Receiving Palliative Care: A Descriptive Study
Bibliographic record
Abstract
CONTEXT: Adequate pain management in frail hip fracture patients receiving nonoperative treatment has been identified as an important issue. Palliative care could be an option to consider to ensure a comfortable end of life for these patients. OBJECTIVES: This study aimed to describe pain relief in frail patients admitted to palliative care following a hip fracture, and the pain management strategies used among them. METHODS: This descriptive monocentric observational study included a retrospective phase, based on a review of medical records, and a prospective phase, by direct observation of patients. Data collection took place within the first five days following admission to palliative care. Pain was assessed with the ALGOPLUS scale. Data on pharmacological and nonpharmacological pain management strategies were collected from medical records. RESULTS: A total of 61 patients with a mean age of 87 years (±7) and severe frailty were included. The proportion of patients with pain at rest ranged from 30% on day 1 to 10% on day 5, and from 71% to 32% during mobilization. The mean oral morphine equivalent daily dose administered ranged from 13.1 mg (±10.7) to 21.9 mg (±16.2). On average, 75% of patients received co-analgesics, and nonpharmacological strategies were applied in 33% of them over the five-day of data collection period. CONCLUSION: Pain remains an issue in frail patients with a nonoperated hip fracture, despite the provision of palliative care. Optimizing pain management, particularly ahead of mobilization, remains a crucial and underexplored area to address for this population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".