Cytokine levels in patients (pts) with colorectal cancer and breast cancer and their relationship to fatigue and cognitive function
Bibliographic record
Abstract
9070 Background: Cytokines have been associated with fatigue and cognitive dysfunction. Here we evaluated plasma cytokine levels in pts with colorectal cancer (CRC) and breast cancer (BC) who were free of evident disease, and in healthy volunteers. Methods: Serum levels of 10 cytokines were measured using a LiquiChip assay on 251 subjects. CRC pts (n=136, ages 23–75) were evaluated at baseline (mean 8 weeks post-surgery [n=107] or before surgery [n=29]), with repeat measures at 6 months (56 post chemotherapy [CT], 14 without CT) and 12 months (32 post CT, 7 without CT). BC pts (n=51, ages 29–60) were within 5 years of diagnosis (33 after adjuvant CT). Healthy volunteers (n=64) had ages 20–62. Cancer pts completed questionnaires for fatigue & QOL (FACT-F), anxiety/depression (GHQ), and perceived cognitive function (FACT-COG); they had neuropsychological assessment. Results: Cytokines were elevated in all cancer groups compared to healthy controls (p-values <0.001; selected data in table ). Values were highest after surgery but remained significantly higher than healthy controls at 6–60 months after diagnosis, with a trend to being higher in cancer patients who had not received CT. There was a trend to elevated cytokines being associated with greater fatigue and cognitive impairment in both CRC and BC, but no association with QOL or anxiety & depression. Conclusions: Cytokine levels were elevated in all cancer groups compared to healthy volunteers and remained elevated up to 5 years post diagnosis; they may be associated with cognitive dysfunction and fatigue. [Table: see text] No significant financial relationships to disclose.
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 | 0.000 | 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".