Tyrosine kinase inhibitors and tumor lysis syndrome in hematologic malignancies: A systemic review
Bibliographic record
Abstract
BACKGROUND: Effective treatments for hematologic malignancies include therapies that target tyrosine kinase (TK) signaling pathways. Tumor lysis syndrome (TLS) is an oncologic emergency that can occur due to rapid turnover following the initiation of treatments for hematologic malignancy. The incidence of TLS is under-reported and it is unclear as to whether TK inhibitors (TKIs) are associated with TLS. OBJECTIVE: To conduct a systematic review to determine the incidence of TLS with TKIs. METHODS: A search was performed using EMBASE, MEDLINE, and Web of Science electronic databases, as well as a manual search of the American Society of Hematology and American Society of Clinical Oncology abstract databases. Keywords included: "tumor lysis syndrome," "tyrosine kinase inhibitors," "lymphoma," and "leukemia." RESULTS: We identified a total of 57 publications that commented on the incidence of TLS with TKIs for hematologic malignancy. Thirty-nine of those publications reported TLS as an adverse event. TLS was described as an adverse event among essentially all the subclasses of TKIs that are used to manage hematologic malignancies. CONCLUSION: The overall number of articles commenting on TLS as an adverse event is sparse and there needs to be more transparency regarding the incidence of TLS when employing newer targeted therapies. Physicians should consider the risk of TLS on an individual basis and the added risk of TLS when using TKIs to treat hematologic malignancy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".