Pharmacovigilance in hospice/palliative care: net effect of gabapentin for neuropathic pain
Bibliographic record
Abstract
INTRODUCTION: Real-world effectiveness of many medications has been poorly researched, including in hospice/palliative care. Directly extrapolating findings from other clinical settings may not yield robust clinical advice. Pharmacovigilance studies provide an opportunity to understand better the net impact of medications. The study aimed to examine immediate and short-term benefits and harms of pregabalin in routine practice for neuropathic pain in hospice/palliative care. METHODS: A consecutive cohort of 155 patients from 62 centres in 5 countries was started on pregabalin and studied prospectively. Data were collected at three time points: baseline; day 7 (immediate, short-term harms); ad hoc reports of any harms ≤21 days; and day 21 (short-term benefits). RESULTS: Median dose for 155 patients at day 21 was 150 mg/24 h. Benefits were reported by 61 patients (39%), of whom 11 (7%) experienced complete pain resolution. Harms were reported by 51 (35%) patients at or before 7 days, the most frequent of which were somnolence, fatigue, cognitive disturbance and dizziness. 10 patients (6%) ceased pregabalin due to harms, but 82 patients (53%) were being treated at 21 days. In regression modelling, people with worse baseline pain derived more benefit (OR=8.5 (95% CI 2.5 to 28.68). CONCLUSIONS: Pregabalin delivered benefit to many patients, with 4 of 10 experiencing pain reductions by 21 days. Harms, occurring in 1 in 3 patients, may be difficult to detect in clinical practice, as they mostly involve worsening of symptoms prevalent at baseline.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".