Cancer-related cognitive impairment as a key contributor to psychopathology in cancer survivors: implications for prevention, treatment and supportive care
Bibliographic record
Abstract
A significant proportion of cancer survivors will experience some form of mental health compromise across domains including mood, anxiety, psychosis, eating disorders, and substance use. This psychopathology within cancer survivors is related to a range of negative outcomes and can also have a substantial negative impact on quality of life. Along with psychopathology, cognitive impairments are also commonly experienced, resulting in deficits in memory, reasoning, decision-making, speed of processing, and concentration, collectively referred to as cancer-related cognitive impairment (CRCI). Within the non-oncology literature, cognitive deficits are consistently demonstrated to be a key transdiagnostic aetiological feature of psychopathology, functionally contributing to the development and perpetuation of symptoms. Whilst there is an acknowledgement of the role mental health concerns might play in the development of and perception of CRCI, there has been limited acknowledgement and research exploring the potential for CRCI to functionally contribute toward the development of transdiagnostic psychopathology in cancer survivors beyond simply psychosocial distress. Given the theoretical and empirical evidence suggesting cognitive deficits to be an aetiological factor in psychopathology, we provide a rationale for the potential for CRCI to be a factor in the development and perpetuation of transdiagnostic psychopathology in cancer survivors. This potential functional association has significant implications for risk identification, prevention, treatment, and supportive cancer care approaches regarding psychopathology in cancer survivorship. We conclude by providing directions for future research in this area.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".