Prescription opioid use before and after heart transplant: Associations with posttransplant outcomes
Bibliographic record
Abstract
Impacts of the prescription opioid epidemic have not yet been examined in the context of heart transplantation. We examined a novel database in which national U.S. transplant registry records were linked to a large pharmaceutical claims warehouse (2007-2016) to characterize prescription opioid use before and after heart transplant, and associations (adjusted hazard ratio, 95% LCLaHR95% UCL) with death and graft loss. Among 13 958 eligible patients, 40% filled opioids in the year before transplant. Use was more common among recipients who were female, white, or unemployed, or who underwent transplant in more recent years. Of those with the highest level of pretransplant opioid use, 71% continued opioid use posttransplant. Pretransplant use had graded associations with 1-year posttransplant outcomes; compared with no use, the highest-level use (>1000 mg morphine equivalents) predicted 33% increased risk of death (aHR 1.101.331.61) in the year after transplant. Risk relationships with opioid use in the first year posttransplant were stronger, with highest level use predicting 70% higher mortality (aHR 1.461.701.98) over the subsequent 4 years (from >1 to 5 years posttransplant). While associations may, in part, reflect underlying conditions or behaviors, opioid use history is relevant in assessing and providing care to transplant candidates and recipients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".