Episodic Memory and Recollection Network Disruptions Following Chemotherapy Treatment in Breast Cancer Survivors: A Review of Neuroimaging Findings
Bibliographic record
Abstract
Long-term memory disturbances are amongst the most common and disruptive cognitive symptoms experienced by breast cancer survivors following chemotherapy. To date, most clinical assessments of long-term memory dysfunction in breast cancer survivors have utilized basic verbal and visual memory tasks that do not capture the complexities of everyday event memories. Complex event memories, including episodic memory and autobiographical memory, critically rely on hippocampal processing for encoding and retrieval. Systemic chemotherapy treatments used in breast cancer commonly cause neurotoxicity within the hippocampus, thereby creating a vulnerability to memory impairment. We review structural and functional neuroimaging studies that have identified disruptions in the recollection network and related episodic memory impairments in chemotherapy-treated breast cancer survivors, and argue for the need to better characterize hippocampally mediated memory dysfunction following chemotherapy treatments. Given the importance of autobiographical memory for a person's sense of identity, ability to plan for the future, and general functioning, under-appreciation of how this type of memory is impacted by cancer treatment can lead to overlooking or minimizing the negative experiences of breast cancer survivors, and neglecting a cognitive domain that may benefit from intervention strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".