Opioid and Benzodiazepine Use in End-Stage Renal Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Chronic pain and psychiatric disorders are common in dialysis patients, but the extent to which opioids and benzodiazepines are used is unclear. We conducted a systematic review to determine the: (1) prevalence of opioid and benzodiazepine use among dialysis patients; (2) reasons for use; (3) effectiveness of symptom control; and (4) incidence of adverse events. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Two authors reviewed all relevant citations in MEDLINE/EMBASE/CINAHL/BIOSIS Previews/Cochrane and hand-searched bibliographies. Studies after 1990 reporting prevalence estimates for opioid and/or benzodiazepine use in ≥50 dialysis patients were included. RESULTS: We identified 15 studies from 12 countries over 1995 to 2006. Sample size ranged from 75 to 12,782. Prevalence of opioid and benzodiazepine use was variable, ranging from 5 to 36% (95% CI, 4.1 to 45.5%; n=10) and 8 to 26% (95% CI, 7.1 to 27.3%; n=9), respectively. Prevalence was positively correlated with years on dialysis. Five studies reported on the same cohorts but gave different prevalence estimates. One study verified medication use through patient interviews. Reasons for use were reported in one study. Effectiveness of pain control varied from 17 to 38%, and 72 to 84% of patients with significant pain had no analgesia (n=2). No study rigorously examined for adverse events. CONCLUSIONS: The prevalence of opioid and benzodiazepine use in dialysis patients is highly variable between centers. Further information is needed regarding the appropriateness of these prescriptions, adequacy of symptom control, and incidence of adverse effects in this population.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".