Integrating Point-of-Care Ultrasound in Hand Clinic: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Purpose: Point-of-care ultrasound (POCUS) is increasingly recognized as a valuable adjunct to physical examination in various clinical settings. Despite this, integration into the routine clinical practice of hand surgeons remains limited. This systematic review and meta-analysis aims to evaluate the diagnostic and clinical utility of POCUS in hand clinics for the assessment and management of common hand pathologies. Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, MEDLINE and Embase were searched. Prospective studies evaluating POCUS use in hand pathologies were included. Two reviewers independently screened, extracted, and assessed data. Meta-analysis of diagnostic performance measures was conducted using a bivariate random effects model when appropriate. Results: Fifteen prospective studies involving 1,217 patients were included. POCUS was most commonly used to assess fractures (76.3%) and tendinous pathologies (20%). For hand fractures, pooled sensitivity and specificity were 81.95% (95% confidence interval [CI]: 78.03% to 85.31%) and 89.39% (95% CI: 87.12% to 91.30%), respectively. Among tendinous pathologies, POCUS demonstrated 100% sensitivity for trigger digits in three of four included studies. Limited evidence also supported high diagnostic performance for nail bed injuries (sensitivity: 97%, specificity: 95%). Conclusions: POCUS demonstrates high diagnostic accuracy for evaluating hand fractures and tendinous injuries while showing promise in the evaluation of nail bed trauma. Heterogeneity in study design, operator expertise, and reference standards was noted. Clinical relevance: The incorporation of POCUS into the clinical practice of hand surgeons, supported by formal training and technological advancements such as artificial intelligence integration, may improve diagnostic efficiency, guide management, and enhance care delivery.
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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 | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".