The effects of physical therapeutic agents on serum levels of stress hormones in patients with osteoarthritis
Bibliographic record
Abstract
To investigate the effects of physical agents on the levels of stress hormones in patients with osteoarthritis (OA).Transcutaneous electrical nerve stimulation, hot packs, and therapeutic ultrasound were applied to the lumbar region and knees of patients with OA. Blood samples were taken for the measurement of the serum levels of glucose, insulin (INS), growth hormone (GH), prolactin (PRL), cortisol (COR), and plasma adrenocorticotropic hormone (ACTH) immediately before and after the 1st session, to investigate the acute effects of those physical agents on the endocrine system. The hormone levels were also measured every 5 sessions in a total of 10 sessions. The treatment response was also evaluated by using the visual analogue scale (VAS), Roland Morris Disability Questionnaire (RMDQ), and Western Ontario and McMaster Universities Arthritis Index (WOMAC) throughout the therapy period.After the 1st session, there was a decrease in INS levels and a mild decrease in PRL levels (P = 0.001 and P < 0.05, respectively). Throughout the 10-session therapy period, the INS levels increased, whereas the ACTH and COR levels decreased (P < 0.05 for all). The VAS-spine, RMDQ, VAS-knee, and WOMAC scores decreased (P = 0.001 for VAS-spine and P < 0.001 for all others). A positive correlation was detected between the changes in serum COR and WOMAC-pain score (P < 0.05).Although the combination therapy caused changes in INS level accompanied with steady glucose levels, the application of physical agents did not adversely affect the hormone levels. The decrease in ACTH and COR levels may be attributed to the analgesic effect of agents and may be an indicator of patient comfort through a central action.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".