Cognitive function and fatigue in cancer patients after chemotherapy: A longitudinal cohort study in patients with colorectal cancer (CRC)
Bibliographic record
Abstract
9099 Background: There is growing evidence that fatigue and cognitive dysfunction can affect cancer survivors. Here we evaluated these symptoms in patients with CRC in a longitudinal prospective study. Methods: Patients with localized CRC were evaluated for cognitive function and fatigue at baseline (mean 8 weeks post-surgery or before neoadjuvant therapy), 6 & 12 months. Group A (Stage III/high risk II) received chemotherapy (CT) and group B (Stage I/II) received no CT. Pts had neuropsychological (NP) assessment with traditional tests and CANTAB, a computerized NP battery. They completed concurrent questionnaires for fatigue & QOL (FACT-F), anxiety/depression (GHQ), and perception of cognitive function (FACT-COG). Blood tests evaluated cytokine levels, blood clotting factors, sex hormones and apolipoprotein genotyping as potential causal factors. Primary endpoints were cognitive function (traditional NP tests) and fatigue. Associations between test results, demographic and disease-related factors were sought. Results: Baseline data are available for 182 pts: 127 group A, and 55 group B, with follow-up at 6 and 12 months for 71 and 39 pts. Mean age was 57 years and 62% were male. At baseline (pre CT): 30% had cognitive impairment on traditional NP tests & 20% on CANTAB; 25% reported moderate fatigue and 10% extreme fatigue. At 6 months there was no significant difference on objective NP testing between the groups or in perceived cognitive impairment (median FACT- COG 82 vs 88, p=0.34). CT pts had more fatigue (median FACT-F 75 vs 91, p<0.001). At 12 months CT pts tend to have more cognitive impairment on traditional NP tests (26% vs 0%, p=.09), more perceived cognitive impairment (13.5% vs 0%, p=.57) & greater fatigue (16% vs 0%). Cytokine levels were elevated in all groups at all time points compared to healthy volunteers. There was a trend to higher cytokine levels with greater fatigue and worse cognitive impairment. Fatigue, QOL and anxiety and depression were highly correlated. Conclusions: Cognitive impairment is present in some pts prior to CT and there is a trend for CT pts to have worse cognitive impairment at 12 but not at 6 months. Fatigue is associated with CT. Cytokine levels remained elevated in all groups compared to healthy volunteers. No significant financial relationships to disclose.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".