Frequency and prediction of non-medical opioid use behaviors (NMOU) among advanced cancer patients referred to a supportive care center (SCC).
Bibliographic record
Abstract
12086 Background: One of the methodological barriers to conducting research on interventions for NOMU (Aberrant Opioid Use Behaviors) among cancer patients is the lack of data on the frequency of this problem. Although the frequency of risk factors has been established by our group and others, not all the patients with risk factors will be diagnosed with having NMOU behaviors, and some patients with no previous risk factors will engage in NMOU. AIM: To characterize the overall frequency of NMOU for a duration of 3 months, as well independent predictors for NMOU. Methods: In this retrospective study, 1558 consecutive patients referred to supportive care clinic (SCC) from 3/18/2016 to 6/6/2018 were reviewed for development of NMOU using established diagnostic criteria. Patients were eligible if they were ≥18 years, had a diagnosis of cancer, and were on opioids for pain for at least a week. All patients were assessed with the Edmonton Symptom Assessment Scale (ESAS), SOAPP-14, and CAGE-AID. Descriptive statistics, spearman correlation coefficient, multivariate analysis were performed. Results: 299 patients (19%) had ≥1 NMOU behavior. The median (IQR) NMOU behavior was 1 (1-2); range 1-10. Most NMOU occurred at 1st and 2nd follow up visits. The most frequent NMOU behavior was unscheduled clinic visit for inappropriate refills. 29/299 (10%) NMOU patients received specialized care for high-risk for aberrant opioid misuse by interdisciplinary team. Results of multivariate logistic regression model showed Marital status (Divorced vs. Married, OR=1.47, 95% CI: 0.98, 2.22, p=0.654 (marginally significant); Single vs. Married, OR=1.68, 95% CI: 1.15, 2.46, p=0.0079), SOAPP (Positive vs. Negative, OR=1.42, 95% CI: 1.05, 1.93, p=0.0238), morphine equivalent daily dose (MEDD) (OR=1.004, 95% CI: (1.003, 1.006), p<0.0001) and ESAS pain (OR=1.11, 95% CI: 1.06, 1.17, p<0.0001) were independently associated with the presence of NMOU during follow-up visits. Conclusions: 19% cancer patients followed at SCC had detectable NMOU behaviors. Being single, SOAPP+, pain severity and high MEDD were independent predictors for NMOU. This information will assist clinicians and investigators designing clinical and research programs in this important field.
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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) | 0.001 | 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".