Patient-reported outcomes in cancer survivors: a single-subject exploratory experimental study of the effects of a yoga therapy intervention
Bibliographic record
Abstract
Cancer is a chronic condition associated with poorer patient-reported outcomes (PROs) post-treatment. Investigating interventions that could improve PROs is important to reduce the symptom burden associated with cancer. We investigated the effects of a yoga therapy (YT) intervention on PROs among adults after cancer treatment. A single-subject exploratory experimental study was conducted to evaluate the effects of a YT intervention comprised of a single 1:1 YT session followed by 6 weekly group-based YT sessions (2-3 participants/group). PROs (cancer-related fatigue [CRF], depression, stress, cognitive function, quality of life [QoL]) were assessed before, during, and after the YT intervention. Data from 20 adults (Mage=55.74, 85% women; Myears since diagnosis=2.83) were analyzed using multilevel modeling. The model for CRF showed significant time, phase, and time-by-phase effects; the time effect indicates levels linearly decreased by 1.59 at each time point across the study, the phase effect indicates levels decreased by 3.88 immediately after 1:1 YT, and the time-by-phase effect indicates a larger decrease in CRF over time once YT was introduced. The model for perceived cognitive impairments, impacts of perceived cognitive impairments on QoL, and functional wellbeing showed significant time effects; levels increased linearly by 2.16, 0.72, and 0.75 at each time point. No improvements were observed for remaining PROs. Whilst results require confirmation in future trials, they support continued investigation into 1:1 and group-based YT to improve specific PROs after cancer treatment. Research on mechanisms through which YT improves PROs and factors that moderate response to YT is needed.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".