Computerized cognitive training in post-treatment hematological cancer survivors: a feasibility study
Bibliographic record
Abstract
BACKGROUND: Computerized cognitive training (CCT) programs have shown some effectiveness in alleviating cognitive symptoms in long-term cancer survivors. For patients presenting with cognitive symptoms in the early post-treatment phase, the benefit of CCT is unclear. To assess the possibility of testing the effectiveness of CCT in the early post-treatment period, our aim was to investigate the feasibility of an 8-week home-based, online CCT intervention among patients who have recently completed treatment for hematological malignancy. METHODS: This study was a single-arm, non-blinded, feasibility study. All participants were provided with the CCT intervention for an 8-week period. Feasibility was evaluated based on participant adherence and patient perceptions of the intervention, assessed through responses to an acceptability questionnaire and semi-structured interviews at the end of the intervention period. RESULTS: The feasibility study included 19 patients who had completed treatment for hematological malignancy at a Canadian tertiary cancer center. Adherence to the CCT intervention was limited, with only one participant meeting the criteria for intervention adherence. At the end of the intervention period, participants characterized the program as easy to follow (92%) and felt well-prepared for how to complete the exercises (100%). In semi-structured interviews, participants highlighted post-treatment barriers to intervention adherence that included symptom burden and competing time demands. Participants also suggested improvements to the intervention that could help maintain adherence despite these barriers, such as fostering a sense of accountability, providing personalized feedback and coaching, and enabling opportunities for peer support. CONCLUSIONS: Participation in CCT can be challenging in the post-treatment period for hematological cancers. Further research on the effectiveness of CCT in this setting may require the implementation of strategies that support participants' engagement with the intervention in the context of symptoms and competing demands, such as establishing a minimum dose requirement and integrating approaches to help promote and sustain motivation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".