Bibliographic record
Abstract
Daratumumab is a recent addition in the treatment of multiple myeloma (MM). Daratumumab is a human monoclonal antibody directed against CD38. Daratumumab has no major side effects on the liver published to date. In this case report we describe a dose dependent drug induced liver injury after the use of daratumumab in the treatment of MM that has not been described previously. To describe the first case report of Daratumumab dose dependent liver injury. Case report A 58-year-old gentleman with IgA kappa MM which was diagnosed in January 2015 and has been refractory to three previous lines of therapy. He was admitted on an outpatient basis to the hospital in August 2016, for the treatment of his refractory IgA kappa MM, with Daratumumab. He had no recent changes in his medications and his baseline liver enzyme profile was normal. Following Daratumumab first full dose with 16mg/kg, the patient became fatigued within 48 hours and there was elevation in liver enzymes with an initial ALT of 1622 and AST of 1672. His Alkaline phosphates was 96 and total Bilirubin was 21. Viral serology (A, B, C, EBV and CMV), Acetaminophen and Ethanol level, Anti-nuclear antibody, anti-mitochondrial antibody and anti-smooth muscle antibody were negative. The dramatic increase in his liver enzymes was attributed to Daratumumab hepatotoxicity. Two days following the first dose, the liver enzymes started to trend down without intervention and gradually decreased to normal level after a period of 20 days. Subsequent doses of Daratumumab were modified at 8 mg/kg without increase in liver enzymes. Daratumumab has a potential hepatotoxic effect that is likely dose dependent. Modified doses of Daratumumab may reduce the risk of liver injury. None
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".