Opioid Monitoring Using Urine Toxicology Screens in Outpatient Oncology Palliative Medicine
Bibliographic record
Abstract
PURPOSE: There is a paucity of real-world data on opioid screening and urine toxicology testing in outpatient oncology palliative medicine. METHODS: This was a retrospective analysis of adult patients with cancer completing ≥ one outpatient palliative medicine visit and the Edmonton Symptom Assessment Scale (ESAS). Patient demographics, the Screener and Opioid Assessment for Patients with Pain-Short Form (SOAPP-SF), ESAS, medications, and urine toxicology screens (UTSs) were collected at baseline and follow-up visits. The primary end point was the frequency and type(s) of noncompliant UTSs (ie, presence of a nonprescribed substance or absence of a prescribed substance). Secondarily, risk factors for noncompliant UTSs were evaluated using univariate and multivariable logistic regression. RESULTS: = .029) were associated with increased odds of a noncompliant UTS. CONCLUSION: More than half of the tested population had noncompliant UTS. Screening and evaluating risk factors for nonmedical opioid use is critical in oncology palliative medicine.
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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 | 0.002 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".