From Bench to Bedside: Hematopoietic Growth Factors (HGFs)- a Cause of Serious and Unanticipated Class Toxicities.
Bibliographic record
Abstract
Abstract Abstract 4555 Introduction HGFs have vastly improved the management of anemia, neutropenia, and thrombocytopenia, but are associated with unexpected toxicities. Methods Toxicities associated with granulocyte, erythroid, and thrombopoietic growth factors were reviewed. Data sources included systematic reviews, clinical trials, guidelines, and materials disseminated by the Food and Drug Administration (FDA), the European Medicines Agency (EMEA), and Canada Health and their advisory committees; product labels and safety notifications disseminated by manufacturers; and utilization data. Data abstracted were thromboembolic complications, immune-mediated cytopenias, bone marrow toxicities, systematic and synergistic toxicities, and regulatory responses. Results Granulopoiesis agents Pulmonary infiltrates were associated with administration of HGFs with bleomycin or chest radiation. Increased myelodysplasia (MDS) or acute myeloid leukemia (AML) risks among chemotherapy (chemo)-treated breast cancer patients were reported. ESAs Increased mortality and tumor progression were seen when cancer patients received ESAs versus placebo; increased mortality and cardiovascular events were reported when chronic kidney disease (CKD) patients received ESAs targeted to complete anemia correction. Between 1998 and 2003, 191 CKD patients developed neutralizing antibodies and pure red cell aplasia (PRCA) following recombinant erythropoietin administration. Thrombopoietins (TPO) TPO receptor agonist administration is associated with bone marrow reticulin and collagen deposition. 3% of healthy volunteers who received repeated rHuMGDF developed neutralizing autoantibodies and severe thrombocytopenia. Conclusions While ESAs, granulopoietic factors, and thrombopoietins have important clinical benefits, they also retain the potential to exhibit serious and unanticipated toxicities. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.003 | 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".