INCREASED PREVALENCE AND ANALYSIS OF RISK FACTORS FOR INDINAVIR NEPHROLITHIASIS
Bibliographic record
Abstract
PURPOSE: Indinavir is a protease inhibitor used for treating HIV-1. The drug is lithogenic and was thought to cause a 3% incidence of kidney stones. We evaluated a cohort of patients positive for HIV on indinavir to determine the incidence of indinavir nephrolithiasis and identify risk factors for indinavir stone formation. MATERIALS AND METHODS: Our cohort study of the prevalence of indinavir nephrolithiasis included 155 patients with HIV for 5,732 patient-weeks. The same cohort was then used for a retrospective chart review to assess patient age, weight, duration of drug use, time to stone formation, CD4 count, creatinine, alanine transaminase, and urinary pH and specific gravity as risk factors for stone formation. RESULTS: We estimated the cumulative incidence of indinavir stone formation by the Kaplan-Meier product limit estimator method. At 78 weeks 43.2% of patients had stones (95% confidence interval [CI] 0.292 to 0.543). Increasing age was the only variable that was a statistically significant predictor of indinavair urolithiasis (relative risk 0.955, 95% CI 0.918 to 0.993, p = 0.0159). The mean duration plus or minus standard deviation of indinavir use was statistically the same in each group (42.5 +/- 27. 2 and 40.3 +/- 27.1 weeks in those without and with stones, respectively) despite the observed mean time to stone formation of 23.0 +/- 19.8 weeks. CONCLUSIONS: The clinical prevalence of indinavir nephrolithiasis is much greater than initially reported. Nephrolithiasis during indinavir use does not appear to induce patients to withdraw from the drug.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".