Outcome implications of benzodiazepine and opioid co‐prescription in kidney transplant recipients
Bibliographic record
Abstract
Abstract The outcomes of benzodiazepine and opioid co‐prescription are not well‐defined in transplant populations. We examined linked national transplant registry and pharmaceutical records to characterize benzodiazepine and opioid use in the years before and after transplant in large US cohort of kidney transplant recipients (2007‐2016; N = 98 620), and associations (adjusted hazard ratio, LCLaHRUCL) with death and graft failure. Among the cohort, 15.6% filled benzodiazepine prescriptions in the year before transplant, and 14.0% filled benzodiazepine prescriptions in the year after transplant (short‐acting, 9.5%; long‐acting, 3.3%; both 1.1%). Use of short‐acting benzodiazepines in the year before transplant was associated with a 22% increased risk of death in the year after transplant (aHR, 1.081.221.38), while use of all classes in the year after transplant was associated with increased risk of death from >1 to 5 years (aHR: short‐acting 1.291.391.48; long‐acting 1.121.251.40; both 1.461.742.07). Recipients who used benzodiazepines were also more likely to fill opioid prescriptions. Recipients who filled both classes of benzodiazepine and the highest level of opioids had a 2.9‐fold increased risk of death compared to recipients who did not use either. Co‐prescription of benzodiazepines and opioids in kidney transplant recipients is associated with increased mortality. Ongoing research is needed to understand mechanisms of risk relationships.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".