Accuracy of Ultrasound and MRI in Preoperative and Postoperative Management of Flexor Tendon Injuries: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Complete and partial flexor tendon lacerations are challenging injuries to diagnose and manage. Imaging modalities can determine grade of laceration, and location of tendon ends preoperatively while detecting presence of adhesions, repair failure, and gap formation postoperatively. Despite these clear advantages, imaging modalities are underutilized because of issues with availability and concerns about accuracy. METHODS: A systematic search of MEDLINE and Embase was conducted to identify papers examining the accuracy of ultrasonography (US) and MRI in preoperative and postoperative management of flexor tendon lacerations. COVIDENCE was used in blinded selection of papers for abstract and full-text review. R Studio was used for meta-analysis of pooled sensitivities and specificities, diagnostic odds ratios, and summary receiver operating curves of both US and MRI. RESULTS: A total of 1197 papers were returned, with 40 being selected after full-text review and 24 being sufficient for statistical analysis. Significant heterogeneity existed for preoperative sensitivity of US and MRI, as well as preoperative specificity of US. MRI was more specific than US in the postoperative period (P < 0.01). Diagnostic odds ratios were >1 for all imaging modalities. The area under the curve for summary receiver operating curves in US preoperative, US postoperative, MRI preoperative, and MRI postoperative were 0.92, 0.81, 0.83, and 0.91, respectively. CONCLUSION: MRI is likely more specific than US in postoperative detection of tendon adhesions, tendon rupture, and gap formation following tendon repair. Notable heterogeneities exist in the literature, highlighting the future need for standardized comparisons of imaging modalities in preoperative management.
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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 | 0.065 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 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".