Low intensity pulsed ultrasound (LIPUS) use for the management of instrumented, infected, and fragility non-unions: a systematic review and meta-analysis of healing proportions
Bibliographic record
Abstract
BACKGROUND: Non-union occurs in approximately 5 to 10% of fracture patients, with certain bones at greater risk of failing to heal. Non-unions have a significant impact on socioeconomic costs and the patients short and long-term quality of life. Low intensity pulsed ultrasound (LIPUS) is a non-invasive therapy for non-union treatment that can improve the long-term outcome. The purpose of this study is to summarize the available literature assessing LIPUS potential to improve the union rate in instrumented, infected, and fragility non-unions. METHODS: A literature search was conducted in the MEDLINE, EMBASE, and CINAHL databases for all relevant literature on the healing rates of LIPUS utilized in instrumented, infected, and fragility non-unions. Study characteristics were summarized for each of the included studies. The percentage of healed patients (healing rate), for instrumented, infected, and fragility fracture non-union patients were pooled from each included study. RESULTS: The literature search identified a total of 326 articles, while searching reference lists and grey literature identified an additional 3 articles. There was a total of 29 articles included in this review, with 20 articles included within the quantitative synthesis of healing rates. The most common design of included studies was case series (17 articles), followed by case reports (9 articles). Studies were primarily retrospective (18 studies), with an additional 10 prospective studies. Non-union healing rates were 82% (95% CI: 76 to 87%) in instrumented, 82% (95% CI: 70 to 95%) in infected, and 91% (95% CI: 87 to 95%) in fragility fracture patients with non-unions. CONCLUSION: This study has provided a thorough overview of the current literature on LIPUS treatment for instrumented, infected, and fragility fracture non-unions. The healing rates for non-unions in these subgroups were comparable to healing rates observed with LIPUS use in general non-union literature. LIPUS treatment should be considered as a conservative non-surgical treatment option to potentially reduce the socioeconomic impact and improve the quality of life of these unfortunate patients. LEVEL OF EVIDENCE: 4 (systematic review of primarily case series data).
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".