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Record W7132870039

Proceedings- High Frequency Magnetic Fields in Treatment ofOsgood-Schlatter Disease

2002· article· en· W7132870039 on OpenAlexaboutno aff
B. Suzic-Todorovic, D. Djordjevic, D. Lekic, E. Strugarevic, B. Nikolic

Bibliographic record

VenueTSpace · 2002
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsnot available
Fundersnot available
KeywordsDiseaseImpulse (physics)JumpingPresentation (obstetrics)Magnetic field
DOInot available

Abstract

fetched live from OpenAlex

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. Powerpoint presentation available in PDF format only

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.021
GPT teacher head0.294
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2002
Admission routes1
Has abstractyes

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