Frequency and factors predictive of aberrant drug behavior in patients presenting to outpatient supportive care center at a comprehensive cancer center.
Bibliographic record
Abstract
10118 Background: Opioid misuse is a growing crisis in cancer patients. 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 associated with ADB using the “Screener and Opioid Assessment for Patients tool” (SOAPP-14) in cancer patients seen at the outpatient supportive care center. We also examined the screening performance of Cut Down, Annoyed, Guilty, and Eye Opener (CAGE-AID) as compared to The SOAPP-14 as a gold standard. Methods: In this retrospective study, 1108 consecutive patients referred to supportive care clinic were reviewed. Patients were eligible if they were ≥18 yrs, have a diagnosis of cancer, and were on opioids for pain for atleast a week. Patients’ demographics, the Edmonton Symptom Assessment Scale (ESAS), SOAPP-14, and CAGE-AID scores were analyzed. ADB+ was defined as SOAPP-14 score ≥7. Descriptive statistics, spearman correlation coefficient, multivariate, and ROC analysis were performed. Results: 703/1108 consults were eligible. A total of 153/703 (22%) were ADB +ve. SOAPP-14 scores were positively correlated with CAGE-AID r = .38, p < 0.001; male gender r = 0.11, p = 0.003; ESAS pain r = 0.11, p = 0.005; ESAS depression r = 0.22, p < 0.001; ESAS anxiety r = 0.22, p < 0.001, and ESAS financial distress r = 0.23, p < 0.001. Multivariate analysis indicated that the odds ratio for ADB +ve was 6.18 in patients with CAGE-AID+ (p < 0.001), 1.8 for male gender (p = 0.007), 1.1/pt. for ESAS anxiety (p = 0.044), and 1.1/pt. for ESAS financial distress (p = 0.007). A CAGE-AID score of 1/4 has a sensitivity of 47%, specificity of 89% positive predictive value 63.6% and negative predictive value 69.2%. Conclusions: Our study suggests that 22% of cancer patients on opioids presenting to supportive care center are at risk of aberrant drug behavior (ADB). Male patients with anxiety, financial distress, and prior alcoholism/illicit drug use are significant predictors of ADB’s. A cut off score of ≥1 out 4 on CAGE-AID questionnaire allows better screening of ADB in outpatient advanced cancer patients. 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 | 0.003 |
| 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 | 0.000 |
| 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".