Exploring the Impact of Nonlinear Dynamical Neurofeedback on Post-Cancer Cognitive Impairment and Cancer-Related Fatigue: Results of Interviews with Breast Cancer Survivors
Bibliographic record
Abstract
Background: Breast cancer survivors may experience persistent cognitive impairment and fatigue after completion of cancer treatment, which negatively impacts their quality of life. Neurofeedback is a novel, non-invasive form of brain training reported to help with symptoms such as pain, fatigue, depression, anxiety, sleep problems, and cognitive decline; however, there is a lack of research exploring its use with cancer survivors. Objective: The objective of this study was to describe experiences of neurofeedback and its impact on the lives of post-treatment breast cancer survivors. Methods: This article describes the qualitative phase of a prospective pilot feasibility trial of a nonlinear dynamical neurofeedback intervention. Study participants had the option to participate in a semi-structured interview at follow-up. A sample of 12 breast cancer survivor clients participated in interviews 5–10 weeks after the completion of 20 sessions of nonlinear dynamic neurofeedback. This qualitative descriptive study employed thematic analysis of interview transcripts. Results: Qualitative analysis revealed two overarching themes of impact and experience and six subthemes: symptom impact, dramatic effect, symptom improvement, enjoyable experience, lack of side effects, and recommend for cancer survivors. Conclusion: Results of this qualitative descriptive study suggest that nonlinear dynamical neurofeedback had a dramatic and meaningful positive effect on persistent symptoms experienced by breast cancer survivors, without any negative side effects. Participants in our sample found the neurofeedback sessions to be enjoyable and recommend that they be offered to all cancer patients. Clinical trials with larger sample sizes are needed to corroborate our findings. Establishing clinical effectiveness could encourage adoption of neurofeedback into routine cancer care and health insurance coverage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".