Benzodiazepine Use among Chronic Pain Patients Prescribed Opioids: Associations with Pain, Physical and Mental Health, and Health Service Utilization
Bibliographic record
Abstract
OBJECTIVE: Benzodiazepines (BZDs) are commonly used by chronic pain patients, despite limited evidence of any long-term benefits and concerns regarding adverse events and drug interactions, particularly in older patients. This article aims to: describe patterns of BZDs use; the demographic, physical, and mental health correlates of BZD use; and examine if negative health outcomes are associated with BZD use after controlling for confounders. SUBJECTS: A national sample of 1,220 chronic noncancer pain (CNCP) patients prescribed long-term opioids. METHODS: We report on baseline data from a prospective cohort study comparing four groups based on their current BZD use patterns. General demographics, pain, mental and physical comorbidity, and health service utilization were examined. RESULTS: One-third (N = 398, 33%) of participants reported BZD use in the past month, and 17% (N = 212) reported daily BZD use. BZD use was associated with: 1) greater pain severity, pain interference with life, and lower feelings of self-efficacy with respect to their pain; 2) being prescribed "higher-risk" (>200 mg oral morphine equivalent) doses of opioids; 3) using antidepressant and/or antipsychotic medications; 4) substance use (including more illicit and injection drug use, alcohol use disorder, and daily nicotine use); and 5) greater mental health comorbidity. After controlling for differences in demographic characteristics, physical and mental health, substance use, and opioid dose, BZD use was independently associated with greater past-month use of emergency health care such as ambulance or accident and emergency services. CONCLUSIONS: CNCP patients using BZDs daily represent a high-risk group with multiple comorbid mental health conditions and higher rates of emergency health care use. The high prevalence of BZD use is inconsistent with guidelines for the management of CNCP or chronic mental health conditions.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".