Frequency, predictors, and outcomes of urine drug testing among patients with advanced cancer on chronic opioid therapy at an outpatient supportive care clinic
Bibliographic record
Abstract
BACKGROUND: Data are limited on the use and outcomes of urine drug tests (UDTs) among patients with advanced cancer. The main objective of this study was to determine the factors associated with UDT ordering and results in outpatients with advanced cancer. METHODS: A retrospective chart review was conducted of 1058 patients who attended an outpatient supportive care clinic from March 2014 to November 2015. Sixty-one patients who were receiving chronic opioid therapy and underwent UDTs were identified. A control group of 120 patients who did not undergo UDTs was selected for comparison. RESULTS: Sixty-one of 1058 patients (6%) underwent UDTs, and 33 of 61 patients (54%) had abnormal results. Multivariate analysis indicated that the odds ratio for UDT ordering was 3.9 in patients who had positive Cut Down, Annoyed, Guilty, and Eye Opener (CAGE) questionnaire results (P = .002), 4.41 in patients aged < 45 years (P < .001), 5.58 in patients who had moderate-to-severe pain (Edmonton Symptom Assessment Scale pain scores ≥4; P < .001), 0.27 in patients with advanced-stage cancer, (P = .008), and 0.25 in patients who had moderate-to-severe fatigue (P = .001). Among 52 abnormal UDT results in 33 patients, the most common opioid findings were prescribed opioids absent in urine (14 of 52 tests; 27%) and unprescribed opioids in urine (13 of 52 tests; 25%). CONCLUSIONS: UDTs were used infrequently among outpatients with advanced cancer who were receiving chronic opioid therapy. Younger age, positive CAGE questionnaire results, early stage cancer or no evidence of disease status, higher pain intensity, and lower fatigue scores were significant predictors of UDT ordering. More than 50% of UDT results were abnormal. More research is necessary to better characterize aberrant opioid use in patients with advanced cancer. Cancer 2016;122:3732-9. © 2016 American Cancer Society.
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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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".