Patients’ experiences with cognitive changes after chemotherapy
Bibliographic record
Abstract
Being diagnosed with cancer and undergoing treatment can be a daunting experience. The side effects of treatment often influence a person's quality of life. One side effect that has been identified more recently is known as "chemobrain." Although attempts have been made to quantify and measure cognitive changes, little attention has been paid to describing the changes from the patient's viewpoint. This investigation was undertaken to understand the impact of cognitive changes on daily living and to identify the strategies patients used to cope with "chemobrain." Thirty-two individuals provided in-depth interviews about their experiences living with cognitive changes. Their descriptions provided clear evidence that the changes could effect daily living, social and work-related activities. About a quarter of the individuals expected the changes to be temporary while the rest were uncertain or expected the change to be permanent. The emotional distress people experienced was linked to whether or not the cognitive changes interfered with their doing something that was of importance to them. Overall, participants used a variety of strategies to cope with the changes. The most frequently identified strategy was "writing everything down." When asked what nurses could do to assist them in managing this side effect, participants emphasized how important it is for them to have information about the potential for cognitive change at the beginning of their treatment.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".