Review: Efficacy and safety of tenofovir disoproxil fumarate in patients with chronic hepatitis B
Bibliographic record
Abstract
Chronic hepatitis B (CHB) is prevalent worldwide. It may cause cirrhosis and hepatocellular carcinoma. Treatment for this condition may need to be lifelong, thus the drugs used must be both efficacious and safe. Clinical trials of tenofovir have demonstrated a good safety profile for this drug and it has potent antiviral properties. However, to better characterize the safety of this drug, the postmarketing surveillance must be taken into account. Clinicians need to be vigilant, as infrequent adverse events may be revealed during this phase. The current review presents a detailed exposé of preclinical and clinical data on tenofovir to increase awareness of possible adverse events and drug-drug interactions, based on the large experience of this drug in human immunodeficiency virus (HIV) treatment (and to date in patients with CHB). Several recommendations that may help the clinician to prevent the development of adverse events associated with tenofovir disoproxil fumarate (TDF) treatment are outlined, along with a suggested surveillance protocol for the timely and proper identification of possible renal and bone toxicity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".