Frequency of Concomitant Use of Gabapentinoids and Opioids among Patients with Cancer-Related Pain at an Outpatient Palliative Care Clinic
Bibliographic record
Abstract
Background: Patients with cancer-related pain use opioids for nociceptive pain, while gabapentinoids are common to treat neuropathic pain. The simultaneous use of opioids with gabapentinoids has been associated with an increased risk of opioid-related death. Objectives: Determine the frequency of combined use of gabapentinoids among patients receiving opioids for cancer-related pain. We also examined if concomitant use of opioids and gabapentinoids together was associated with increased scores of fatigue and drowsiness on the Edmonton Symptom Assessment Scale (ESAS) compared to patients on opioids. Design: Retrospective study of patients on opioids and opioids plus gabapentinoids at their third visit to the outpatient Supportive Care Center. Results: We found that 48% (508/1059) of patients were on opioids. Of these patients, 51% (257/508) were on opioids only, and 49% (251/508) were on opioids plus gabapentinoids. The median (interquartile range [IQR]) morphine equivalent daily dose for patients on opioids was 75 (45, 138) mg, and opioids plus gabapentinoids was 68 (38, 150) mg ( p = 0.94). The median (IQR) gabapentinoid equivalent daily dose was 900 (300, 1200) mg. The median (IQR) for ESAS-fatigue in patients on opioids was 5 (3, 7), and opioids plus gabapentinoids was 5 (3, 7) ( p = 0.27). The median (IQR) for ESAS-drowsiness in patients on opioids was 3 (0, 5), and opioids plus gabapentinoids was 3 (0, 6) ( p = 0.11). Conclusion: Almost 50% of advanced cancer patients receiving opioids for pain were exposed to gabapentinoids. Maximal efforts should be made to minimize potential complications from the concomitant use of opioids with gabapentinoids.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".