Immune checkpoint inhibitors and cancer-related cognitive decline: a propensity score matched analysis in active chemotherapy patients
Bibliographic record
Abstract
Background: We investigated whether 1-year trajectories of cancer-related cognitive decline (CRCD) would be different in patients with chemotherapy combined with immune checkpoint inhibitors (chemoICI group) as compared with chemotherapy alone (chemo group). Methods: Participants scheduled with or without ICI were prospectively recruited from three academic hospitals and followed up for 1 year in four sessions. Subjective and objective CRCD were measured by Perceived Cognitive Impairment (PCI) and Montreal Cognitive Assessment (MoCA), respectively. Primary endpoints were MoCA and PCI score changes and minimal clinically important difference (MCID), which was defined as threshold for meaningful impairment events. Propensity score matching (PSM) was performed for group comparison using logistic regression with covariates including age, cancer stage, and baseline cognitive scores. Linear mixed models adjusted for repeated measures. Results: Out of 1557 recruited patients PSM yielded 460 patient pairs (1:1). Mean PCI and MoCA scores of both groups reached MCID at 12-month session in both groups. In chemoICI, MoCA score changes were significantly lower in the 12-month session, and PCI score changes were lower in the 6, 9, and 12-month sessions than chemo (P<0.05). One-year meaningful impairment events risks were 0.44 and 0.56 in chemoICI, significantly higher than that of chemo (0.35 and 0.38, P<0.01). Significant differences were found in mean event-free survival time in patients with and without irAE in chemoICI subgroup analysis. Conclusions: Our findings suggest that combining chemotherapy with ICIs may exacerbate CRCD compared to chemotherapy alone. However, reliance on screening tools and self-reported measures limits definitive conclusions. Future studies incorporating comprehensive neuropsychological assessments are warranted. This study underscores the importance of using comprehensive cognitive assessments in future research to better understand the impact of ICIs on cognitive function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".