Low-intensity pulsed ultrasonography versus electrical stimulation for fracture healing: a systematic review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: To best inform evidence-based patient care, it is often desirable to compare competing therapies. We performed a network meta-analysis to indirectly compare low intensity pulsed ultrasonography (LIPUS) with electrical stimulation (ESTIM) for fracture healing. METHODS: We searched the reference lists of recent reviews evaluating LIPUS and ESTIM that included studies published up to 2011 from 4 electronic databases. We updated the searches of all electronic databases up to April 2012. Eligible trials were those that included patients with a fresh fracture or an existing delayed union or nonunion who were randomized to LIPUS or ESTIM as well as a control group. Two pairs of reviewers, independently and in duplicate, screened titles and abstracts, reviewed the full text of potentially eligible articles, extracted data and assessed study quality. We used standard and network meta-analytic techniques to synthesize the data. RESULTS: Of the 27 eligible trials, 15 provided data for our analyses. In patients with a fresh fracture, there was a suggested benefit of LIPUS at 6 months (risk ratio [RR] 1.17, 95% confidence interval [CI] 0.97-1.41). In patients with an existing nonunion or delayed union, ESTIM had a suggested benefit over standard care on union rates at 3 months (RR 2.05, 95% CI 0.99-4.24). We found very low-quality evidence suggesting a potential benefit of LIPUS versus ESTIM in improving union rates at 6 months (RR 0.76, 95% CI 0.58-1.01) in fresh fracture populations. CONCLUSION: To support our findings direct comparative trials with safeguards against bias assessing outcomes important to patients, such as functional recovery, are required.
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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 | 0.037 | 0.092 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| 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".