Random urine drug testing among patients receiving opioid therapy for cancer pain
Bibliographic record
Abstract
BACKGROUND: There is limited information regarding the true frequency of nonmedical opioid use (NMOU) among patients receiving opioid therapy for cancer pain. Data to guide patient selection for urine drug testing (UDT) as well as the timing and frequency of ordering UDT are insufficient. This study examined the frequency of abnormal UDT among patients with cancer who underwent random UDT and their characteristics. METHODS: Demographic and clinical information for patients with cancer who underwent random UDT were retrospectively reviewed and compared with a historical cohort that underwent targeted UDT. Random UDT was ordered regardless of a patient's risk potential for NMOU. Targeted UDT was ordered on the basis of a physician's estimation of a patient's risk for NMOU. RESULTS: In all, 552 of 573 eligible patients (96%) underwent random UDT. Among these patients, 130 (24%) had 1 or more abnormal results; 38 of the 88 patients (43%) who underwent targeted UDT had 1 or more abnormal results. When marijuana was excluded, 15% of the random group and 37% of the targeted group had abnormal UDT findings (P < .001). It took a shorter time from the initial consultation to detect 1 or more abnormalities with the random test than the targeted test (median, 130 vs 274 days; P = .02). Abnormal random UDT was independently associated with younger age (P < .0001), male sex (P = .03), Cut Down, Annoyed, Guilty, and Eye Opener-Adapted to Include Drugs positivity (P = .001), and higher Edmonton Symptom Assessment System anxiety (P = .01). CONCLUSIONS: Approximately 1 in 4 patients receiving opioids for cancer pain at a supportive care clinic who underwent random UDT had 1 or more abnormalities. Random UDT detected abnormalities earlier than the targeted test. These findings suggest that random UDT is justified among patients with cancer pain.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".