Impacts of center and clinical factors in antihypertensive medication use after kidney transplantation
Bibliographic record
Abstract
Hypertension guidelines recommend calcium channel blockers (CCBs), thiazide diuretics, and angiotensin-converting-enzyme inhibitors/angiotensin receptor blockers (ACEi/ARBs) as first-line agents to treat hypertension. Hypertension is common among kidney transplant (KTx) recipients, but data are limited regarding patterns of antihypertensive medication (AHM) use in this population. We examined a novel database that links national registry data for adult KTx recipients (age > 18 years) with AHM fill records from a pharmaceutical claims warehouse (2007-2016) to describe use and correlates of AHM use during months 7-12 post-transplant. For patients filling AHMs, individual agents used included: dihydropyridine (DHP) CCBs, 55.6%; beta-blockers (BBs), 52.8%; diuretics, 30.0%; ACEi/ARBs, 21.1%; non-DHP CCBs, 3.0%; and others, 20.1%. Both BB and ACEi/ARB use were significantly lower in the time period following the 2014 Eighth Joint National Committee (JNC-8) guidelines (2014-2016), compared with an earlier period (2007-2013). The median odds ratios generated from case-factor adjusted models supported variation in use of ACEi/ARBs (1.51) and BBs (1.55) across transplant centers. Contrary to hypertension guidelines for the general population, KTx recipients are prescribed relatively more BBs and fewer ACEi/ARBs. The clinical impact of this AHM prescribing pattern warrants further study.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".