Prescription patterns of opioids and non‐steroidal anti‐inflammatory drugs in the first year after living kidney donation: An analysis of U.S. Registry and Pharmacy fill records
Bibliographic record
Abstract
Abstract We examined a novel database linking national donor registry identifiers to records from a US pharmaceutical claims warehouse (2007‐2015) to describe opioid and NSAID prescription patterns among LKDs during the first year postdonation, divided into three periods: 0‐14 days, 15‐182 days, and 183‐365 days. Associations of opioid and NSAID prescription fills with baseline factors were examined by logistic regression (adjusted odds ratio, LCLaORUCL). Among 23,565 donors, opioid prescriptions were highest during days 0‐14 (36.6%), but 12.6% of donors filled opioids during days 183‐365. NSAID prescriptions rose from 0.5% during days 0‐14 to 3.3% during days 183‐365. Women filled opioids more commonly than men, and black donors filled both opioids and NSAIDs more commonly than white donors. After covariate adjustment, significant correlates of opioid prescription fills during days 183‐365 included obesity (aOR,1.241.381.53), less than college education (aOR,1.191.311.43), smoking (aOR,1.331.451.58), and nephrectomy complications (aOR,1.111.291.49). NSAID prescription fills in year 1 were not associated with differences in estimated glomerular filtration rate, incidence of proteinuria or new‐onset hypertension at the first and second year postdonation. Prescription fills for opioids and NSAIDs for LKDs varied with demographic and clinic traits. Future work should examine longer‐term outcome implications to help inform safe analgesic regimen choices after donation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".