Association Between Tenofovir Exposure and Reduced Kidney Function in a Cohort of HIV-Positive Patients: Results From 10 Years of Follow-up
Bibliographic record
Abstract
BACKGROUND: Some studies have shown that tenofovir disoproxil fumarate (TDF), a drug widely used in highly active antiretroviral therapy, is associated with kidney dysfunction, but the magnitude of the effect and its clinical impact is still being debated. Our objective was to evaluate the association between long-term TDF exposure and kidney dysfunction in a cohort of 1043 human immunodeficiency virus-positive patients followed up for 10 years and to quantify the loss in estimated glomerular filtration rate (eGFR) in patients exposed to TDF in comparison with those exposed to other antiretroviral therapies. METHODS: Adjusted hazard ratios (HR) and odds ratios (OR) for the association between TDF and kidney dysfunction (defined as eGFR <90 mL/min/1.73 m(2)) were calculated using the Cox proportional hazards model and generalized estimating equations. Mean loss in eGFR attributable to TDF by cumulative years of exposure was estimated using linear regressions. RESULTS: Tenofovir exposure increased the risk of kidney dysfunction by 63% (HR, 1.63; 95% confidence interval, 1.26-2.10). The cumulative eGFR loss directly attributable to TDF after 1, 2, 3, and 4 years of TDF exposure was -3.05 (P = .017), -4.05 (P = .000), -2.42 (P = .023), and -3.09 mL/min/1.73 m(2) (P = .119), respectively, which shows that most of the loss occurred during the first years of exposure. CONCLUSIONS: In this cohort, TDF exposure was associated with reduced kidney function, but the loss in eGFR attributable to TDF is relatively mild in a long-term perspective.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".