Cognitive impairment in hematology patients planned for chimeric antigen receptor T-cell therapy
Bibliographic record
Abstract
Background Chimeric antigen receptor T-cell (CAR-T) therapy is used to treat several types of relapsed and refractory hematological malignancies and is associated with cognitive side-effects. The accurate diagnosis of cognitive impairment following CAR-T requires knowledge of baseline cognitive status prior to the therapy.Research design and methods Adult patients with advanced hematologic or solid organ malignancies underwent cognitive assessment, including a self-report questionnaire of psychopathology and subjective cognitive function, prior to receiving CAR-T. A subset of individuals also completed the Montreal Cognitive Assessment (MoCA) to examine utility of cognitive screening.Results Of 60 patients included, 16 (27%) had cognitive impairment, with six unique patterns of dysfunction. Memory impairment was the most common finding (15%). Impaired patients were more likely to have B-cell acute lymphoblastic leukemia (p = 0.024, BF10 = 9.30), be younger (p = 0.007, BF10 = 7.76), have bone marrow involvement (p = 0.037, BF10 = 5.18), or have evidence of psychopathology (p = 0.004, BF10 = 31.30). Analyses did not support the utility of cognitive screening. Of those patients who completed a self-report measure of psychopathology, nine (16%) were elevated on at least one symptom domain.Conclusions The findings demonstrate a broad spectrum of cognitive and psychological symptoms, emphasizing the importance of baseline evaluation for detecting cognitive symptoms that might arise after CAR-T.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".