Cancer‐related cognitive impairment and associated factors in a sample of older male oral‐digestive cancer survivors
Bibliographic record
Abstract
OBJECTIVE: This study examines the demographic and clinical variables associated with cancer-related cognitive impairment (CRCI) in a sample of older, male, oral-digestive cancer survivors at VA Medical Centers in Boston and Houston. METHODS: A two-time point, longitudinal design was used, with cognitive assessment conducted at 6 and 18 months post-diagnosis. Using ANCOVA, the cognitive functioning of 88 older adults with head and neck, esophageal, gastric, or colorectal cancers was compared with that of 88 healthy controls. Paired t-tests examined cognitive change over time in the cancer group. Hierarchical linear regression examined variables potentially associated with cognitive impairment at 18 months. RESULTS: Forty-eight percent of cancer patients exhibited cognitive impairment 6 months post-cancer diagnosis, and 40% at 18 months. Cancer survivors were impaired relative to controls on measures of sustained attention, memory, and verbal fluency at 18 months, controlling for age. Older age, low hemoglobin, and cancer-related PTSD were associated with worse cognition at 18 months. CONCLUSIONS: CRCI is more frequent in older adults than reported in studies of younger adults and may be more frequent in men. Potential areas of intervention for CRCI include psychotherapy for cancer-related PTSD, treatment of anemia, and awareness of particularly vulnerable cognitive domains such as sustained attention, memory, and verbal fluency.
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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 | 0.001 | 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 | 0.000 |
| 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".