眼泪
体格检查
系统回顾
医学
心理学
梅德林
外科
生物
生物化学
作者
Andrew Dakkak,Michael K. Krill,Matthew L. Krill,Benedict U. Nwachukwu,Frank McCormick
标识
DOI:10.1177/1941738120936232
摘要
Context: There is a renewed interest in diagnosing and treating subscapularis tears, but there is a paucity of clinical guidance to optimize diagnostic decision-making. Objective: To perform a literature review to evaluate advanced maneuvers and special tests in the diagnosis of subscapularis tears and create a diagnostic algorithm for subscapularis pathology. Data Sources: PubMed, MEDLINE, Ovid, and Cochrane Reviews databases. Study Selection: Inclusion criteria consisted of level 1 and 2 studies published in peer-reviewed scientific journals that focused on physical examination. Study Design: Systematic review. Level of Evidence: Level 2. Data Extraction: Individual test characteristics (bear hug, belly press, lift-off, Napoleon, and internal rotation lag sign) were combined in series and in parallel to maximize clinical sensitivity and specificity for any special test evaluated in at least 2 studies. A secondary analysis utilized subjective pretest probabilities to create a clinical decision tree algorithm and provide posttest probabilities. Results: A total of 3174 studies were identified, and 5 studies met inclusion criteria. The special test combination of the bear hug and belly press demonstrated the highest positive likelihood ratio (18.29). Overall, 3 special test combinations in series demonstrated a significant impact on posttest probabilities. With parallel testing, the combination of bear hug and belly press had the highest sensitivity (84%) and lowest calculated negative likelihood ratio (0.21). Conclusion: The combined application of the bear hug and belly press physical examination maneuvers is an optimal combination for evaluating subscapularis pathology. Positive findings using this test combination in series with a likely pretest probability yield a 96% posttest probability; whereas, negative findings tested in parallel with an unlikely pretest probability yield a 12% posttest probability.
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