High Frequency Magnetic Fields in Treatment of Osgood- Schlatter Disease
Bibliographic record
Abstract
We are studied efficacy of high frequency magnetic fields (HFMFs) in treatment of Osgood-Schlatter disease (OSD) including X-rays signs. THELF system apparatus has been used for treatments on 32 young sportsmen (9-15 years). 18 boys and 14 girls, with HFMFs (base frequency 27.125 MHz, with different impulse repetition frequency of 640 Hz and 320 Hz, and absorbed power 3-5 VA). Injured sportsmen were players with large numbers of high or long jumping such as in the basketball, volleyball, fieldball and rhythmical gymnastics. Time of every daily treatment was 30 mm (640 Hz) and 30 mm (320 Hz) during 28 days but the number of treatment on an average was 28±2. Using The McGill Pain Questionnaire indicated that in mostly cases subjective complaints has been disappeared in a month from of beginning of treatments, which give sportsmen’s opportunity to begin to exercise. In two months they were strongly and in better shape to exercise for a final match. In all cases are confirmed with positive X-rays signs. These results suggest that HFMFs is may be very important method of treatment Osgood-Schlatter disease. Using HFMFs in treatment of Osgood-Schlatter disease we made excellent progress in more rapidly recovering of injured young sportsmen’s.
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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) | 0.002 | 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".