Frequency and factors predicting the risk for aberrant opioid use in patients receiving outpatient palliative care at a comprehensive cancer center.
Bibliographic record
Abstract
228 Background: Opioid misuse is a growing crisis among patients with chronic pain. Cancer patients at risk of aberrant drug behaviors (ADB) are frequently underdiagnosed in routine cancer care. The aim of this study was to determine the frequency and factors predicting risk for Aberrant Opioid and Drug use among Patients receiving Outpatient Supportive Care Consultation at a Comprehensive Cancer Center Methods: In this retrospective study, 690 consecutive patients referred to a supportive care clinic were reviewed. Patients were eligible if they were ≥18 years, had a diagnosis of cancer, and were on opioids for pain for atleast a week. All patients were assessed with the Edmonton Symptom Assessment Scale (ESAS), SOAPP-14, and CAGE-AID. At risk patients with aberrant opioid behavior (+Risk) was defined as SOAPP-14 score ≥7. Descriptive statistics, spearman correlation coefficient, multivariate analysis were performed. Results: 690/752 consults were eligible. A total of 135(20%)were +risk. 69(11%) were CAGE-AID +.SOAPP-14 scores were positively associated with CAGE-AID p < 0.001; male gender p = 0.007; ESAS pain p = < 0.006; ESAS depression p < 0.001; ESAS anxiety, p < 0.001, and ESAS financial distress p = < 0.001. Multivariate analysis indicated that the odds ratio for +Risk was 2.47 in patients with CAGE-AID+ (p < 0.001), 1.95 for male gender (p = 0.005), 1.11 per point for ESAS anxiety (p = 0.019), and 1.1 per point. for ESAS financial distress (p = 0.02). Conclusions: 20% of cancer patients on opioids presenting to supportive care center are at risk of aberrant drug behavior. Male patients with anxiety, financial distress, and prior alcoholism/illicit drug use are significant predictors of +Risk. Further research to effectively manage these patients is needed.
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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 | 0.001 |
| 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".