Population-Based Study of Risk of AKI with Levetiracetam
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Regulatory agencies warn about the risk of AKI with levetiracetam use on the basis of information from case reports. We conducted this study to determine whether new levetiracetam use versus nonuse is associated with a higher risk of AKI. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: This was a population-based retrospective cohort study of adults with epilepsy in Ontario, Canada. Patients who received a new outpatient prescription for levetiracetam between January 1, 2004 and March 1, 2017 were matched to two nonusers on stage of CKD, recorded seizure in the prior 90 days, and logit of a propensity score for levetiracetam use. The primary outcome was a hospital encounter (emergency department visit or hospitalization) with AKI within 30 days of cohort entry. Secondary outcomes were AKI within 180 days and change in the concentration of serum creatinine. We assessed the primary outcome using health care diagnosis codes. We evaluated the change in the concentration of serum creatinine in a subpopulation with laboratory measurements. RESULTS: We matched 3980 levetiracetam users to 7960 nonusers (mean age 55 years, 51% women). Levetiracetam use was not significantly associated with a higher risk of AKI within 30 days (13 [0.33%] events in levetiracetam users and 21 [0.26%] events in nonusers [odds ratio, 1.24; 95% confidence interval, 0.62 to 2.47]). Similarly, there was no significant association with AKI within 180 days (odds ratio, 0.70; 95% confidence interval, 0.43 to 1.13). The change in the concentration of serum creatinine did not significantly differ between levetiracetam users and nonusers. CONCLUSIONS: In this population-based study levetiracetam use was not associated with a higher risk of AKI. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2018_12_11_Yau_Podcast.mp3.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".