Frequency of concomitant use of opioids and psychoactive medications among cancer patients referred to outpatient palliative care at a comprehensive cancer center.
Bibliographic record
Abstract
240 Background: There are potential severe effects when patients taking opioids receive other psychoactive medications. However, such combinations are sometimes necessary in palliative care. The purpose of this study was to determine the frequency of concomitant use of opioids + psychoactive medications in cancer patients referred to our outpatient palliative care center. Methods: Retrospective data obtained from consecutive consults was analyzed to determine the frequency of patients on opioids alone versus concomitant opioids + psychoactive medications at first presentation to our clinic. Association of type of medication with demographics and baseline characteristics was evaluated by Wilcoxon rank sum test for continuous variables and Chi-square (Fisher's exact) test for categorical variables. Results: Among 541 consecutive consult visits, 365 (67%) patients were taking opioids at the time of referral to our clinic: 209 (57%) were on opioids alone while 156 (43%) were on concomitant opioids + psychoactive medications [69 (44%) were on Opioid + Benzodiazepine, 46 (30%) were Opioid + Antidepressants, 41(26%) were on both). Patients in the concomitant groups were on higher Morphine Equivalent Daily Dose (MEDD, p = 0.007), had higher Edmonton Symptom Assessment Scores (ESAS) for pain (p = 0.017), anxiety (p < 0.001), depression (p < 0.001) and spiritual pain (p = 0.03). Conclusions: A large proportion (156, 43%) of cancer patients referred to outpatient palliative care was on concomitant opioids + psychoactive medications. These patients were on higher doses of opioids with higher levels of pain and psycho-social distress at the time of first presentation. Further studies are required to better understand the clinical implications of concomitant use of opioids + psychoactive medications in such patients.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".