Bibliographic record
Abstract
The purpose of this study is to find out the effects of the therapeutic sports massage(TSM) applied to the patients with osteoarthritis of the knee by measuring, assessing and analyzing the changes in intensity and unpleasantness of muscle pains before and after TSM In the therapeutic sports massage program, such methods as effleurage, petrissage and deep transverse friction were selected as traditional massage treatments frequently used for muscles with pain. Effleurage and petrissage were applied for 20 minutes in total before and after deep transverse friction treatment. After TSM, the McGill pain questionnaire word list(MPQWL), verbal rating scale(VRS), visual analogue scale(VAS) were used to measure the degree of the pain on the patients. The major findings from this study are as follows; 1. There was significant decrease in the numerical values of VAS, VRS and MPQWL after TSM(p＜.05). 2. There was significant decrease in the intensity and unpleasantness of pains after TSM(p＜.05). 3. From the analysis into chronological changes in the intensity and unpleasantness of pains before and after TSM with ANOVA, it became evident that the longer the period of treatment was, the higher the pains decreases drastically, while significant difference was shown in the intensity and unpleasantness of pains(p＜.05). Summed up, it can be generally concluded that TSM is an effective treatment to rid the patients with knee osteoarthritis of pains safely and promptly.
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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 | 0.000 | 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) | 0.004 | 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".