Pain Improvement After Healing Touch and Massage in Breast Cancer: an Observational Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Healing Touch (HT) and Oncology Massage (OM) are nonpharmacologic pain interventions, yet a comparative effectiveness study has not been conducted for pain in breast cancer. PURPOSE: This breast cancer subgroup analysis compared the effectiveness of HT vs. OM on pain. SETTING: The research occurred at an outpatient setting at an academic hybrid, multi-site, community-based cancer institute and Department of Supportive Oncology across four regional locations. PARTICIPANTS: Breast cancer outpatients along the cancer continuum who experienced routine clinical, nonexperimentally manipulated HT or OM. RESEARCH DESIGN: The study was an observational, retrospective, comparative effectiveness post hoc subanalysis of a larger dataset. Patients reporting pain < 2 were excluded. Pre- and posttherapy pain scores and differences were calculated. Logistic regression modeled posttherapy pain by modality, adjusting for pretherapy pain. The proportions experiencing ≥ 2-point (clinically significant) pain reduction were compared with chi-square tests. INTERVENTION: The study focused on the first session of either HT or OM. MAIN OUTCOME MEASURES: Pre- and posttherapy pain (range: 0 = no pain to 10 = worst possible pain). RESULTS: = .072). CONCLUSIONS: Both HT and OM were associated with clinically significant pain improvement. Future research should explore attitudes toward the modalities and potential influence of cancer stage and treatment status on modality self-selection.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".