Geriatric assessment in patients aged 70 and over considered for CAR-T therapy: Adescriptive study
Bibliographic record
Abstract
Abstract Geriatric oncology addresses the challenges of treating elderly cancer patients, who are often undertreated due to complex care needs. CAR-T therapy, effective for some hematologic cancers, presents significant side effects that complicate its use in patients over 70. Comprehensive geriatric assessment may help guide treatment decisions in this population. This study aims to describe the profile of elderly patients referred for potential CAR-T therapy, assess the impact of the geriatric evaluation on treatment decisions, examine the occurrence of ICANS and cytokine release syndrome in relation to frailty, and monitor clinical outcomes such as hospitalizations and mortality. This retrospective study will include patients aged 70 and over referred to the Quebec Geriatric Clinic for pre-treatment assessment before May 2025. Data will be collected from medical records, including patient profiles, initial treatment plans, recommendations from geriatric oncology, final therapeutic plans, and clinical outcomes. No complex statistical analysis will be performed due to the limited sample size (approximately 30 patients). Results will be presented descriptively. We expect that the geriatric evaluation will lead to modifications in treatment plans and identify risk factors for severe adverse effects, helping to refine eligibility criteria for CAR-T therapy in elderly patients. The findings could enhance clinical practices by promoting personalized care in geriatric oncology. This study seeks to demonstrate the importance of integrating a complete geriatric evaluation into treatment planning for elderly cancer patients, optimizing care and decision-making for CAR-T therapy.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".