Mid-term neurocognitive outcomes after CAR T cell therapy in central nervous system lymphomas: statistical analysis plan
Bibliographic record
Abstract
Background: Chimeric antigen receptor (CAR) T cells are an innovative therapeutic option for hematological malignancies. The indications of anti-CD19 CAR T cells have recently been extended to the treatment of central nervous system lymphomas. They can cause an immune effector cell-associated neurotoxicity syndrome, which has been reported with similar rates and short-term prognosis in central nervous system and systemic lymphomas. However, longer-term neurocognitive outcomes in central nervous system lymphomas after treatment with CAR T cells remain unknown. Clarifying them is especially important because of the frequent pre-existing cognitive impairment in these patients, which could theoretically either worsen or improve after treatment. This study therefore aims to characterize mid-term neurocognitive outcomes following CAR T cell therapy in central nervous system lymphomas.Methods: In this study, demographic, clinical, and neurocognitive longitudinal data about patients treated with anti-CD19 CAR T cells for a central nervous system lymphoma will be retrospectively collected from the French LOC network database. The primary outcome will be the change in Montreal cognitive assessment scores between baseline and 12 months post-treatment. The data analysis framework will include paired Student’s t-tests and linear regression models, as well as more advanced methods such as factor analysis, iterative principal component analysis, and trajectory modeling. Missing data will be managed by multiple imputation. The dataset will be synthesized in order to be released in an open version.Discussion: The proposed statistical framework, combining conventional analysis methods with more advanced machine learning techniques, has the potential to enhance the understanding of neurocognitive trajectories after CAR T cell therapy in central nervous system lymphomas. Identifying potentially distinct neurocognitive profiles and prognostic factors may inform both current clinical practice and further research into therapeutic options in this condition.
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 | 0.058 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".