Characterizing changes in executive functions and performance in daily activities after chemotherapy: A pre-post mixed-methods study protocol
Bibliographic record
Abstract
BACKGROUND: Impairments in higher cognitive abilities (termed executive functions (EF) are common among individuals with cancer following chemotherapy and may impact their performance of daily activities. Our aim is to better understand the changes in EF and the impact on performance in daily activities of individuals with cancer pre- and post-chemotherapy. METHODS: A convergent parallel mixed-method pre-post experimental design. Qualitative and quantitative data will be collected pre- and post-chemotherapy (12 weeks following chemotherapy commencement). Participants will be adult candidates for chemotherapy who are newly diagnosed with non-central nervous system malignancy, stages I-III. The Canadian Occupational Performance Measure will assess the performance of daily activities; secondary measures include EF, cognitive functioning, fatigue, and emotional well-being. Qualitative data will be collected via open-ended questions. Pre- and post-chemotherapy, quantitative and qualitative data will be analyzed separately and merged into an overall interpretation. It is expected that, pre-chemotherapy, no difficulties in performing daily activities will be revealed. Post-chemotherapy EF impairments will be apparent and their impact on the performance of daily activities will be identified. CONCLUSIONS: Integrating quantitative and qualitative measures will contribute to a comprehensive understanding of the individuals' cognitive needs and may enable the development of effective interventions to minimize deterioration in daily activities after chemotherapy.
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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 | 0.039 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".