Ultrasound for Fracture Healing: Current Evidence
Bibliographic record
Abstract
Low-intensity pulsed ultrasound (LIPUS) is a relatively new technique for the acceleration of fracture healing in fresh fractures and nonunions. It has a frequency of 1.5 MHz, a signal burst width of 200 micros, a signal repetition frequency of 1 kHz, and an intensity of 30 mW/cm2. In 1994 and 1997, two milestone double-blind randomized controlled trials revealed the benefits of LIPUS for the acceleration of fracture healing in the tibia and radius. They showed that LIPUS accelerated the fracture healing rate from 24% to 42% for fresh fractures. Some literature, however, has shown no positive effects. The beneficial effect of acceleration of fracture healing by LIPUS is considered to be larger in the group of patients or fractures with potentially negative factors for fracture healing. The incidence of delayed union and nonunion is 5% to 10% of all fractures. For delayed union and nonunion, the overall success rate of LIPUS therapy is approximately 67% (humerus), 90% (radius/radius-ulna), 82% (femur), and 87% (tibia/tibia-fibula). LIPUS likely has the ability to enhance maturation of the callus in distraction osteogenesis and reduce the healing index. The critical role of LIPUS for fracture healing is still unknown because of the heterogeneity of results in clinical trials for fresh fractures and the lack of controlled trials for delayed unions and nonunions.
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 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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".