Integrative Oncology Trials in the Real World: Assessing the Pragmatism of an Ongoing Integrative Oncology Trial of Mindfulness and <i>T'ai Chi</i> / <i>Qigong</i>
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to highlight features of pragmatic real-world integrative oncology research by applying the PRagmatic Explanatory Continuum Indicator Summary (PRECIS-2) criteria to an ongoing integrative oncology clinical trial. The ongoing trial is a preference-based randomized comparative effectiveness trial of mindfulness-based cancer recovery (MBCR) versus t'ai chi/qigong (TCQ) for cancer survivors (the Mindfulness and T'ai Chi for Cancer Health [MATCH] study). The primary outcome of the MATCH study is distress, and secondary outcomes are quality of life, sleep disturbance, and physical functioning. The clinical trial is being undertaken at tertiary care cancer centers across two sites in Canada: Calgary (AB) and Toronto (ON), with a sample of 600 cancer survivors who have finished all cancer treatments and are distressed. METHODS AND RESULTS: The MATCH trial was scored on the explanatory-pragmatic continuum for each of the nine domains of the PRECIS-2 criteria on a scale of 1-5, and was rated as more explanatory than pragmatic, despite initial design efforts being more pragmatic. Areas that were least pragmatic were methods of recruitment, follow-up, and intervention delivery. The more pragmatic areas were setting, outcomes, and data analysis. CONCLUSIONS: More efforts toward conducting pragmatic trials are needed in the field of integrative oncology, as cancer-care institutions and policy makers are looking for sustainable interventions within already established treatment models. The PRECIS-2 criteria can help researchers meet these goals in the planning stages of trial development.
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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 | 0.388 | 0.518 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".