The impact of dance as a non-pharmacological adjuvant therapy cancer survivors: a clinical trial
Bibliographic record
Abstract
This study aimed to investigate the impact of dance as a non-pharmacological adjuvant therapy on the quality of life (QoL), pain sensation, and depression of female cancer survivors. Method: We conducted a parallel, open-label, randomized, controlled clinical trial where cancer patients were invited to experience dance as a language. The intervention comprised two dance group classes per week for 20 weeks involving creative dance processes and light to moderate physical exercises. The participants were randomized into two groups – control (did not undergo the dance classes) and intervention (underwent the dance classes) – and answered questionnaires before, during, and after the intervention. We assessed the QoL (Functional Assessment of Cancer Therapy General), pain perception (Visual Analog Scale and McGill Pain Questionnaire), and depression (Hamilton Depression Rating Scale). Results: The statistical data analyses revealed that the intervention and control groups did not present statistical differences in age, cancer type, stage of disease, surgical treatment, and scapular and pelvic involvement. The results showed an improvement in the intervention group’s QoL regarding the affective, miscellaneous, sensory, and total dimensions and decreased pain perception and depression. Conclusion: This clinical trial presented dance as a complementary non-pharmacological adjunct therapy for cancer survivors' treatment, improving quality of life and decreasing pain perception and depressive processes. Implications for cancer survivors: The practice of dance as a language is a valid intervention to help female cancer survivors face the disease's physical and psychosocial effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".