The Pattern of Gabapentin Use in a Tertiary Palliative Care Unit
Bibliographic record
Abstract
BACKGROUND: Little is known about current practice in using the anticonvulsant gabapentin in the management of cancer-related neuropathic pain. OBJECTIVES: The main objective of this study was to describe the pattern of gabapentin use as an adjuvant analgesic for cancer-related neuropathic pain in patients admitted to a tertiary palliative care unit. METHODS: A retrospective medical chart review for 150 patients admitted to a tertiary palliative care unit over a period of 10 months. Abstracted data included patient characteristics, primary diagnoses, pain scores, and the type and dose of opioids and other adjuvants used. RESULTS: Of the 147 patients with a cancer diagnosis, 45 (31%) had neuropathic pain. Of those, 22 (49%) received gabapentin. The final daily dose of gabapentin ranged from 100 mg-3,000 mg (mean: 941 mg +/- 665 mg; median: 900 mg). Gabapentin was discontinued in 10 patients (46%). Suggested adverse effects included sedation (n = 4), dizziness (n = 1), and bitter taste (n = 1). The change in pain scores was not significantly different in those who continued on gabapentin compared to those who discontinued it. CONCLUSIONS: The retrospective nature of the study and the small sample size render solid conclusions difficult to make. However, gabapentin was discontinued in approximately half the patients who received it, and often when only modest doses were used. Similar studies from other centres may improve understanding of current practices and aid in designing future clinical trials on the subject.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".