Non-steroidal anti-inflammatory drugs for pain in hospice/palliative care: an international pharmacovigilance study
Bibliographic record
Abstract
OBJECTIVES: To describe the current, real-world use of non-steroidal anti-inflammatory drugs for pain and the associated benefits and harms. METHODS: A prospective, multicentre, consecutive cohort pharmacovigilance study conducted at 14 sites across Australia, Aotearoa/New Zealand and the UK including hospital, hospice inpatient and outpatient services. Pain scores and harms were graded using the National Cancer Institute Common Terminology Criteria for Adverse Events at baseline, 2 days and 14 days. Ad-hoc safety reporting continued until day 28. RESULTS: Data were collected from 92 patients between March 2018 and October 2021. Most patients had cancer (91%) and were coprescribed opioids (90%). At 14 days, 83% of patients had benefit from non-steroidal anti-inflammatory drugs and 22% had harm. The most common harms were nausea (8%), vomiting (3%), acute kidney injury (3%) and non-gastrointestinal bleeding (3%); only 2% were severe and no patients ceased their non-steroidal anti-inflammatory drugs due to toxicity. Overall, 65% had benefit without harm and 3% had harm without benefit. CONCLUSIONS: Most patients benefited from non-steroidal anti-inflammatory drugs with only one in five patients experiencing tolerable harm. This suggests that short-term use of non-steroidal anti-inflammatory drugs in patients receiving palliative care is safer than previously thought and may be underused.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".