Symptom Reduction in Advanced Cancer from Multi-session Mindful Breathing: A Randomised Controlled Trial
Bibliographic record
Abstract
Abstract Background: Mindfulness-based interventions have shown promising effects among patients with advanced cancer and other health conditions. While evidence of symptom reduction in palliative care from a single session of mindful breathing is available, data on symptom reduction from multi-session mindful breathing remains unavailable. The objective of this study was to determine the efficacy of multi-session mindful breathing in symptom reduction among patients with advanced cancer. Methods: Adult patients with advanced cancer who scored ≥4 in at least two or more symptoms based on the Edmonton Symptom Assessment Scale (ESAS) were recruited from January to March 2020. Participants were randomly assigned to receive either four daily sessions of 30-minute mindful breathing and standard care (intervention) or standard care alone (control). Results: There were statistically significant reductions in the total ESAS scores following all four sessions in the intervention group (n1 = 40: z1 = -5.09, p < 0.001; z2 = -3.77, p < 0.001; z3 = -4.38, p < 0.001; z4 = -3.27, p < 0.05). For the control group, statistically significant reductions in the total ESAS scores were seen only after sessions 1 and 3 (n2 = 40: z1 = -4.04, p < 0.001; z3 = -4.53. p < 0.001). Conclusions: Our result provides evidence that four daily sessions of 30-minute mindful breathing may be effective in reducing multiple symptoms rapidly in advanced cancer patients. Trial registration: NCT 05910541, date of registration 9th June 2023. (Retrospectively registered)
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".