Acute Tumor Lysis Syndrome Secondary to Hydroxyurea in Acute Myeloid Leukemia
Bibliographic record
Abstract
OBJECTIVE: To report 2 cases of acute tumor lysis syndrome (ATLS) associated with hydroxyurea treatment. CASE SUMMARY: A 79-year-old woman diagnosed with chronic lymphocytic leukemia presented with an acute blastic transformation. She was promptly hydrated, started on allopurinol, and treated with hydroxyurea. About 24 hours later, her biochemistry panel showed parameters consistent with ATLS when compared with pretreatment levels. The second case was a 76-year-old man newly diagnosed with acute myeloid leukemia presenting with high blast fraction. He was given appropriate hydration and allopurinol prophylaxis, and was started on high-dose hydroxyurea treatment. He became symptomatic after 12 hours and results of his blood work were consistent with ATLS. DISCUSSION: ATLS is a well-known metabolic disturbance that occurs after cell destruction of rapidly growing tumors. In standard doses, hydroxyurea leads to cell death in the S phase and is not thought to cause significant cell lysis. However, in large doses, it possibly acts by a different mechanism and had a direct cytolytic effect associated with ATLS in our patients. CONCLUSIONS: Although ATLS caused by hydroxyurea appears to be rare, patients at risk should be closely monitored for this complication. An objective causality assessment using the Naranjo probability scale revealed that the adverse drug reaction was probable between ATLS and hydroxyurea therapy in these 2 patients.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".