Predicting the risk for aberrant opioid use behavior in patients receiving outpatient supportive care consultation at a comprehensive cancer center
Bibliographic record
Abstract
BACKGROUND: Opioid misuse is a growing crisis. Patients with cancer who are at risk of aberrant drug behaviors are frequently underdiagnosed. The primary objective of this study was to determine the frequency and factors predicting a risk for aberrant opioid and drug use behaviors (ADB) among patients who received an outpatient supportive care consultation at a comprehensive cancer center. In addition, the screening performance of the Cut Down-Annoyed-Guilty-Eye Opener (CAGE) questionnaire adapted to include drug use (CAGE-AID) was compared with that of the 14-item Screener and Opioid Assessment for Patients With Pain (SOAPP-14) tool as instruments for identifying patients at risk for ADB. METHODS: In total, 751 consecutive patients with cancer who were referred to a supportive care clinic were reviewed. Patients were eligible if they had diagnosis of cancer and had received opioids for pain for at least 1 week. All patients were assessed using the Edmonton Symptom Assessment Scale (ESAS), the SOAPP-14, and the CAGE-AID. SOAPP scores ≥7 (SOAPP-positive) were used to identify patients who were at risk of ADB. RESULTS: Among the 729 of 751 (97%) evaluable consults, 143 (19.6%) were SOAPP-positive, and 73 (10.5%) were CAGE-AID-positive. Multivariate analysis revealed that the odds ratio of a positive SOAPP score was 2.3 for patients who had positive CAGE-AID scores (P < .0001), 2.08 for men (P = .0013), 1.10 per point for ESAS pain (P = .014), 1.13 per point for ESAS anxiety (P = .0015), and 1.09 per point for ESAS financial distress (P = .012). A CAGE-AID cutoff score of 1 in 4 had 43.3% sensitivity and 90.93% specificity for screening patients with a high risk of ADB. CONCLUSIONS: The current results indicate a high frequency of an elevated risk of ADB among patients with cancer. Men and patients who have anxiety, financial distress, and a prior history of alcoholism/illicit drug use are at increased risk of ADB.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".