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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".