Deranged cytokine levels are linked to cancer-related cognitive impairment in lymphoma patients receiving R-CHOP chemotherapy
Bibliographic record
Abstract
Cancer-related cognitive impairment (CRCI) is a significant issue commonly observed following chemotherapy treatment. The study aimed to investigate the changes in cognitive function and their association with IL-6, IL-1β, and IL-10 levels before and after R-CHOP chemotherapy over six cycles. Seventy chemotherapy naïve, newly diagnosed lymphoma patients were enrolled. Cognitive functions and inflammatory cytokines were assessed at baseline (TP1), after 3rd cycle (TP2), and after 6th cycle (TP3). Patients, with mean age of 44.17 ± 13.67 years, showed significantly increased levels of IL-6 and IL-1β and decreased IL-10 levels over time (p < .001). On the Montreal Cognitive Assessment (MoCA), scores of domains such as executive functioning (p = .002), attention (p < .001), language (p < .001), recall (p = .005), and orientation (p < .001) significantly decreased post six cycles of R-CHOP chemotherapy. Correlation analysis at TP2 indicated a positive association between elevated IL-6 levels with a decrease in MoCA scores indicating a decline in cognitive function (ρ = 0.68, p < .001). At TP3, no association of MoCA scores with IL-6 and IL-1β was observed. Decreased IL-10 levels showed a weak association with decreased MoCA scores at TP2 and TP3 (ρ = 0.2, p = .09; for TP3, ρ = 0.16, p = .17), but this was not significant. In summary, the findings of the present study highlight significant cognitive decline and changes in inflammatory cytokine levels following six cycles of R-CHOP. Objective cognitive assessments may be done to detect CRCI in patients treated with R-CHOP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".