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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".