Healing of fracture nonunions treated with low-intensity pulsed ultrasound (LIPUS): A systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Bone fractures fail to heal and form nonunions in roughly 5% of cases, with little expectation of spontaneous healing thereafter. We present a systematic review and meta-analysis of published papers that describe nonunions treated with low-intensity pulsed ultrasound (LIPUS). METHODS: Articles in PubMed, Ovid MEDLINE, CINAHL, AMED, EMBASE, Cochrane Library, and Scopus databases were searched, using an approach recommended by the Methodological Index for Non-Randomized Studies (MINORS), with a Level of Evidence rating by two reviewers independently. Studies are included here if they reported fractures older than 3 months, presented new data with a sample N≥12, and reported fracture outcome (Heal/Fail). RESULTS: =62). Hypertrophic nonunions benefitted more than biologically inactive atrophic nonunions. An interval without surgery of <6months prior to LIPUS was associated with a more favorable result. Stratification of nonunions by anatomical site revealed no statistically significant differences between upper and lower extremity long bone nonunions. CONCLUSIONS: LIPUS treatment can be an alternative to surgery for established nonunions. Given that no spontaneous healing of established nonunions is expected, and that it is challenging to test the efficacy of LIPUS for nonunion by randomized clinical trial, findings are compelling. LIPUS may be most useful in patients for whom surgery is high risk, including elderly patients at risk of delirium, or patients with dementia, extreme hypertension, extensive soft-tissue trauma, mechanical ventilation, metabolic acidosis, multiple organ failure, or coma. With an overall average success rate for LIPUS >80% this is comparable to the success of surgical treatment of non-infected nonunions.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.029 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".