Effectiveness and safety of aromatherapy massage for knee osteoarthritis
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to evaluate the effectiveness and safety of aromatherapy massage in the treatment of knee osteoarthritis. METHODS: To collect relevant literature, we will research following databases: PubMed, Web of Science, Scopus, Cochrane Library, Embase, China National Knowledge Infrastructure, China Science and Technology Journal Database and Wanfang Data the time is from inception to December 30, 2021, and the language is limited to Chinese and English. In addition, we will retrieve other literature resources, including the Chinese Clinical Trial Register, conference papers. Two reviewers will independently complete the literature screen and data extraction, and quality assessment of the included studies will be independently completed by 2 other researchers. The primary outcomes included the Western Ontario and McMaster Universities Osteoarthritis Index scale, the visual analog scale, symptom score, Lysholm knee scoring scale, adverse events, and adverse reactions as secondary outcomes would be assessed. RevMan V.5.4.1 software will be used for meta-analysis, and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) will be used to assess the quality of evidence. RESULTS: This systematic review will be showed a high-quality synthesis to evaluate the efficacy and safety of aromatherapy massage in the treatment of knee osteoarthritis, providing reference for the safe and effective treatment of knee osteoarthritis. CONCLUSION: This study provides evidence of whether aromatherapy massage is effective.Systematic review registration: INPLASY202210010URL: https://inplasy.com/inplasy-2022-1-0010/.
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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".