Patient-Controlled Analgesia for Managing Pain in Adults Receiving Palliative Care: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Pain remains a significant concern in palliative care settings. Patient-controlled analgesia (PCA) is an opioid delivery system that allows patients to decide when to receive a personally titrated opioid dose. This method of opioid administration effectively and safely manages pain and allows autonomy over one's care. OBJECTIVE: To understand the extent of evidence regarding PCA for pain management among adults in palliative care settings. METHODS: Systematic searches of PubMed, CINAHL, Embase, and MEDLINE identified 421 articles published in English between 2009 and 2024. The following data was extracted from eligible articles: source, author, study purpose, location (country and care setting), sample, design and methodology, participant characteristics, and relevant results. RESULTS: Five studies met inclusion criteria. Findings include information on the PCA devices, rationale for administration, efficacy, and safety, adverse events, and author-identified next steps. Overall, PCA use was found to be safe and effective, sometimes even preferable to other opioid administration regimens. CONCLUSION: This review provides insights into optimizing pain management for cancer patients, especially in advanced stages of illness. Findings highlight the minimal literature available regarding PCA use in palliative care settings, particularly the complete absence across noncancer diagnoses.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".