Bibliographic record
Abstract
Massage therapy has grown as a profession, and its use in the traditional medical setting is rapidly increasing. Massage therapy has been shown to have positive physical, psychological, and biochemical effects. The aim of this study is to review the reasons patients choose breast massage therapy and the benefits of breast massage therapy. Ten female patients of 18 years and older participated and completed the survey. Of the ten patients, the average age was 37.5 years. Most common reasons for choosing breast massage include: improve overall health (90%) and overall well-being (80%), relieve muscle tension (80%), reduce stress (70%), and increase size/firmness/elasticity (70%). Almost all patients reported an improvement in quality of life. Bust size increased for nine patients. Most patients reported a reduction in breast pain, reduction in breast swelling, increase in breast size, increase in breast firmness, improved skin elasticity, improved physical health, improved psychological health, and improved overall health and well-being. Breast massage has positive effects on physical and psychological health and well-being. Its use is increasingly sought after to improve breast health and overall health and wellness. Physicians, healthcare providers, and patients should consider breast massage therapy as complementary and alternative medicine.
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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".