Immunological and Psychological Benefits of Aromatherapy Massage
Bibliographic record
Abstract
This preliminary investigation compares peripheral blood cell counts including red blood cells (RBCs), white blood cells (WBCs), neutrophils, peripheral blood lymphocytes (PBLs), CD4(+), CD8(+) and CD16(+) lymphocytes, CD4(+)/CD8(+) ratio, hematocrit, humoral parameters including serum interferon-gamma and interleukin-6, salivary secretory immunoglobulin A (IgA). Psychological measures including the State-Trait Anxiety Inventory (STAI) questionnaire and the Self-rating Depression Scale (SDS) between recipients (n = 11) of carrier oil massage and aromatherapy massage, which includes sweet almond oil, lavender oil, cypress oil and sweet marjoram oil. Though both STAI and SDS showed a significant reduction (P < 0.01) after treatment with aromatherapy and carrier massage, no difference between the aromatherapy and control massage was observed for STAI and SDS. Aromatherapy, in contrast to control massage, did not significantly reduce RBC count or hematocrit. However, aromatherapy massage showed a significant (P > 0.05) increase in PBLs, possibly due to an increase in CD8(+) and CD16(+) lymphocytes, which had significantly increased post-treatment (P < 0.01). Consequently, the CD4(+)/CD8(+) ratio decreased significantly (P < 0.01). The paucity of such differences after carrier oil massage suggests that aromatherapy massage could be beneficial in disease states that require augmentation of CD8(+) lymphocytes. While this study identifies the immunological benefits of aromatherapy massage, there is a need to validate the findings prospectively in a larger cohort of patients.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".