Serial testing to assess cognitive function in patients with cancer being treated with immunotherapy.
Bibliographic record
Abstract
TPS1644 Background: The advent of immunotherapy (IT) has revolutionized lung cancer treatment, becoming a primary modality from Stages Ib to IV. While the short-term side effects of IT are well documented, its long-term impact on cognitive function are under explored. This is a crucial area of investigation given the significant impact of cognitive health on the quality of life in cancer survivors. Preclinical investigations suggest neurocognitive alterations in IT-treated patients, evidenced by increased microglial activation and cytokine release, contributing to observed cognitive deficits such as impaired cued fear memory and object recognition. This study leverages the Montreal Cognitive Assessment (MoCA) to investigate these potential cognitive changes in a clinical setting, aiming to provide crucial insights into the long-term effects of IT. Methods: This single-center, open-label pilot study enrolls patients with various cancer types and stages undergoing IT. Exclusion criteria include prior history of underlying cognitive dysfunction, dementia, depression or psychiatric illness, history of brain metastasis or radiation. The study compromises three cohorts: those receiving IT alone, those undergoing chemo-immunotherapy, and a control group with no active treatment (in remission with prior history of cancer and have received IT in the past). Each of the IT and chemo-immunotherapy groups will include 72 patients, while the control group will consist of 40 participants. Baseline cognitive function is assessed using the MoCA scale, with follow-up assessments at 3 and 6 months from baseline. Patient enrollment commenced in April 2022, with 4 patients enrolled in the IT group, 16 in the chemo-IT group, and 32 in the control group. The primary objective is to assess the Cognitive function change in each individual group of patients. The secondary objective is to assess change of MoCA scores after 3 months and 6 months in patients receiving immunotherapy alone in lung cancer patients. The changes in MoCA scores will be plotted and summarized with mean change, standard deviation of change and standard errors of mean change. MoCA scores at baseline and at 6 months will be compared using paired t-tests. The study will analyze the rate of change in MoCA scores over time, comparing outcomes across the treatment cohorts. Clinical trial information: NCT06160700 .
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".