Spectrum of Immune Checkpoint Inhibitor Anemias: Results From a Single Center, Early-Phase Clinical Trials Case Series Experience
Bibliographic record
Abstract
Immune checkpoint inhibitor anemias (ICI-A) are a rare entity which can be potentially life-threatening without prompt identification. The goal of the study is to characterize the presentation, evaluation, and outcomes of ICI therapy in early phase clinical trial setting to guide future research and to develop standardized care guidelines. Retrospective chart review of 333 patients who participated in early phase clinical trials at the University of Texas MD Anderson Cancer Center revealed four cases with ICI-A between 2016 and 2020. We identified a spectrum of four cases which included ICI-related autoimmune hemolytic anemias, hemophagocytic lymphohistiocytosis and thrombotic microangiopathy as a result of combinatory investigational therapies involving ICI. Patient presentation, evaluation, bone marrow pathology, interventions, and clinical course were reviewed. The median time to onset of hematological immune-related adverse events (heme-irAEs) in this retrospective series was 3.5 weeks (2 - 6 weeks). One patient had pre-existing untreated chronic lymphocytic leukemia. Glucocorticoids are an effective first-line treatment in most patients although most patients were not rechallenged but successfully had complete recovery and pursued further non-immunotherapy-based therapies. Cognizance of ICI-A in clinical trial setting is paramount to early recognition of heme-irAEs. Further research is needed to identify and stratify risk factors during clinical trial enrollment and optimal management strategies for immune-mediated hematologic toxicities.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".