Systematic urine drug testing for detecting and managing opioid misuse among chronic noncancer pain patients in primary care—The HARMS Program: A retrospective chart review of 77 patients
Bibliographic record
Abstract
The prevalence of opioid abuse has reached an epidemic level. National guidelines recommend safer opioid prescribing practices, including potentially monitoring patients with urine drug testing (UDT). There is limited research evidence sur-rounding the use of UDT in the context of chronic noncancer pain (CNCP). We evaluated the efficacy of systematic, randomized UDT to detect and manage opioid misuse among patients with CNCP in primary care. The Marathon Family Health Team (MFHT) designed and implemented a clinic-wide, randomized UDT program called the HARMS (High-yield Approach to Risk Mitigation and Safety) Program. This retrospective chart review includes 77 CNCP patients being pre-scribed opioids, who were initially stratified by their prescriber as "low-risk." Each month, 10 percent of patients were selected for a random UDT with double testing (immunoassay and liquid chromatography-mass spectrometry). The pri-mary outcome measure was UDT leading to a change in management plan. Of the 77 patients in the study, 55 (71 per-cent) completed at least one UDT during the 12-month study period. Overall, 22 patients had aberrant results. UDT led directly to changes in management in 15 of those patients. Four of those 15 patients were escalated to an addictions program, two were tapered from opioids with informed discussion, and nine were escalated to the high-risk monitoring stream. The results of this study show that in low-risk CNCP patients prescribed opioids, applying systematic UDT in a primary care setting is effective for detecting high risk behaviors and addiction, and altering management. Further re-search is needed with larger numbers using a prospective study design.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".