Opioid screening and urine toxicology results in outpatient oncology palliative medicine.
Bibliographic record
Abstract
e24068 Background: Opioid misuse is a major public health issue. Given widespread opioid prescribing in cancer patients (pts), screening for potential misuse is critical. There is lack of real-world data on opioid screening and urine toxicology testing in outpatient oncology palliative medicine. Methods: This is a retrospective clinical analysis of adult cancer pts previously consented for a pharmacogenomics specimen collection study between August 2019-March 2020. Pts completing ≥ 1 outpatient palliative medicine visit with at least half undergoing urine toxicology screening (UTS) per standard practice were included. Pt demographics, medication(s), UTS results, symptoms using Edmonton Symptom Assessment Scale, and opioid screening using Screener and Opioid Assessment for Patients with Pain - Short Form (SOAPP-SF) were collected at baseline and follow up visits, if available. The primary endpoint was the frequency and type(s) of non-compliant (NC) UTS. Secondarily, risk factors for NC UTS were evaluated using univariate and multivariate logistic regression. Results: Of 189 pts (632 visits), 113 underwent UTS, 125 SOAPP-SF, and 75 had both. The median age was 56, 56% were female, 58% white, 40% black, 48% had stage IV disease, and median pain score was 7. More black pts (72%) underwent UTS compared to white pts (53%) (p = 0.001). The mean age of pts with a UTS was 53 compared to 59 in those without UTS (p = 0.002). Oxycodone was the most prescribed drug (N = 125). Median SOAPP-SF was 3 (range 0-11); 38% had a score ≥ 4 (considered high risk). About half (54%; N = 61) who underwent a UTS were NC. Of these, 32 had 1 NC UTS, whereas 29 had 2 or more. The most common reason was presence of a substance not prescribed (N = 44 pts and 128 results), whereas 33 pts (53 results) were NC for substance(s) not present but prescribed. Four had presence of marijuana only and 21 with marijuana plus another NC substance; presence of cocaine and alcohol were the 2nd and 3rd most frequent aberrant result. Of those with a NC UTS and SOAPP-SF score (N = 44), 59% had a score ≥ 4. In univariate analyses, SOAPP-SF ≥ 4 (p = 0.004), nausea (p = 0.05), depression (p = 0.02), anxiety (p = 0.01), and prescriptions for antidepressants (p = 0.006), acetaminophen (p = 0.03), and/or dronabinol (p = 0.04), were associated with NC UTS. In multivariate analyses, SOAPP-SF Q4 (use of illegal drugs) (OR 2.86, 95% CI 1.64 to 5.02; p < 0.001) and prescription with muscle relaxants (OR 2.90, 95% CI 1.19 to 7.09; p = 0.019) were associated with increased odds of a NC UTS. Conclusions: About half of those undergoing UTS were NC. SOAPP-SF Q4 and prescription with muscle relaxants were associated with a NC UTS. Overall, pt demographics (e.g. younger, more female, more black patients, severe pain) varied from the typical cancer population. Screening using SOAPP-SF, UTS, pain contracts, prescription drug monitoring databases, and evaluating pt-specific risk factors is important to reduce opioid misuse risk.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".