Effects of Low-Intensity Pulsed Ultrasound Therapy on Fracture Healing
Bibliographic record
Abstract
OBJECTIVE: This systematic review and meta-analysis was performed to identify the clinical trials relevant to the effects of low-intensity pulsed ultrasound (LIPUS) on bone regeneration. DESIGN: We searched five international electronic databases including MEDLINE (1966-June 2010), and PubMed, EMBase, Cumulative Index to Nursing and Allied Health, and Cochrane (1980-June 2010) to identify the relevant studies on the effects of LIPUS on bone healing. The inclusion criteria were human clinical trial, all types of bones, fractures, and outcome measurements, LIPUS application, and English language. Overall, 260 potentially eligible abstracts were identified, and 65 articles were retrieved in full text. Of the 65 studies, 23 met the inclusion criteria and were critically appraised by two raters independently using the PEDro quality measurement method. The results of all eligible studies were categorized in three groups: fresh fractures, delayed or nonunions, and distraction osteogenesis. Seven trials among fresh fracture trials were identified eligible for meta-analysis because of the varieties of outcome measurements and clinical situations. The time of the third cortical bridging (increase in density or size of initial periosteal reaction) in radiographic healing was our common criteria for the meta-analysis. RESULTS: The time of third cortical bridging was statistically earlier following LIPUS therapy in fresh fractures (mean random effect, 2.263; 95% CI, 0.183-4.343, P = 0.033). CONCLUSIONS: LIPUS can stimulate radiographic bone healing in fresh fractures. Although there is weak evidence that LIPUS also supports radiographic healing in delayed unions and nonunions, it was not possible to pool the data because of a paucity of sufficient studies with similar outcome measures.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".