Effect of reiki on people with advanced cancer: a randomized controlled trial
Bibliographic record
Abstract
Introduction: Reiki is an Integrative and Complementary Health Practice (PICS), recognized and incorporated into the table of procedures of Brazil's Unified Health System (SUS). Objective: To evaluate the effects of applying Reiki in relieving the symptoms of people with advanced cancer who are hospitalized. Methodology: Randomized controlled study, approved by the Research Ethics Committee, to verify the effect of applying Reiki to people with advanced cancer, admitted to a large, highly complex university hospital located in the interior of the state of São Paulo, Brazil. Fourteen participants were selected from the clinical oncology ward and randomly divided into two groups - Reiki and control - with seven participants each. For data collection, an anamnesis was taken, blood pressure and heart rate were measured, saliva was collected to check cortisol levels; the Edmonton Symptom Assessment System (ESAS), Hospital Anxiety and Depression Scale (HAD-S) and Karnofsky Performance Status (KPS) were applied; Reiki was applied for 21 minutes to participants in the Reiki group and, 30 minutes after Reiki, the scales were reapplied and salivary cortisol was collected again. Results: The Mann-Whitney U-test showed a statistically significant difference for the sleep variable (p<0.007) in the intervention group when compared to the control group. Conclusion: The results showed that Reiki improved the sleep of the participants in this randomized controlled trial and demonstrated that, despite the small caseload, the methodology used is valid for studying the impact of Reiki on the symptoms of people with advanced cancer.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".