Do magnetic field applications lead to improved bone union in light of Evidence-Based Medicine principles? Analysis of the scientific evidence from basic science to clinicalresearch
Bibliographic record
Abstract
Background: One of the widespread indications for using magnet therapy is impaired bone union or an attempt to accelerate the physiological process of osteogenesis.However, it must be noticed that the practical use of magnetic fields in patients after bone fractures overtakes clear clinical recommendations and indisputable scientific evidence.Aims: This article attempts to estimate the current state of knowledge on the effectiveness of magnetotherapy in the claimed range of injuries to the movement system. Material and methods:The critical literature review analyzed bibliographic data for the past ten years.The resources of the following medical search engines were used -PubMed, MEDLINE, Physiotherapy Evidence Database (PEDro), and Web of Science Core Collection.Results: Both basic and clinical studies confirm the effectiveness of these physical treatments after bone fractures.However, it is difficult to say that the strength and level of evidence is high and satisfactory.According to our findings, the average PEDro score for the cited papers is 5.56, which could be a more satisfactory result.Randomized clinical trials with the highest rate (7-10 points on the Physiotherapy Evidence Database scale) are still needed.Magnetic field treatments can be used, although they only support standard management.Conclusions: At this stage, it seems that for clinical purposes such as stimulating bone union and reducing pain, the most recommended is the use of a magnetic field with treatment parametersmagnetic induction of 1-10 mT, frequency up to 50 Hz, rectangular or sinusoidal waveform, single treatment time of 20-30 minutes, 5-7 treatments per week for several to several weeks. 17
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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 | 0.027 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".