No Increase in Adverse Events During Aliskiren Use Among Ontario Patients Receiving Angiotensin-Converting Enzyme Inhibitors or Angiotensin-Receptor Blockers
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Some evidence suggests that the direct renin inhibitor aliskiren may increase the risk of severe hyperkalemia, stroke, or acute kidney injury (AKI) when prescribed with angiotensin-converting enzyme inhibitors (ACEi's) or angiotensin-receptor blockers (ARBs). The extent to which concomitant treatment increases the risk of these outcomes in routine clinical practice is unknown. We addressed this issue with the use of administrative databases. METHODS: We established a cohort of Ontarians treated with an ACEi or an ARB. Within this cohort, we conducted 3 case-control studies. Cases were patients hospitalized with 1 of 3 outcomes (hyperkalemia, AKI, or stroke). In each analysis, we identified up to 5 matched control subjects for each case. Conditional logistic regression was used to examine the association between hospitalization for each outcome and the use of aliskiren in the preceding 60 days. RESULTS: Among 903,346 patients aged 66 years and older treated with an ACEi or ARB during the 28-month study period, we identified 4235 hospitalized with hyperkalemia, 18,231 hospitalized with AKI, and 8283 hospitalized with stroke. After extensive multivariable adjustment, aliskiren therapy was not associated with a significant increase in the risk of hospitalization for hyperkalemia, AKI, or stroke. We found similar results in stratified analyses of patients with and without a history of chronic kidney disease, diabetes, or heart failure. CONCLUSIONS: Among community-dwelling patients aged 66 years and older receiving therapy with an ACEi or an ARB, aliskiren use was not associated with hospitalization for hyperkalemia, AKI, or stroke.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it