From Discovery to Decision Impact in Precision Sports Medicine: A Review-Level Translational Evidence Map

Abstract

Precision sports medicine now integrates genetic, molecular, wearable, imaging, biomechanical, and artificial intelligence data. However, it is still unclear how strong the evidence must be to inform judgments about athlete management. Using PubMed, Scopus, and Web of Science across six predetermined categories, this study created a structured review-level translational evidence map. Of the 7,147 unique records that underwent title and abstract screening after deduplication, 944 were retained as potentially eligible. Athlete relevance, precision-domain fit, complementarity, recency, domain coverage, and redundancy management were then used to prioritize a prespecified principal corpus of 75 peer-reviewed systematic reviews and meta-analyses. These 75 reviews do not represent the entire set of eligible reviews. The corpus was finalized by a single author/reviewer who applied predefined review-level grades for translational maturity, consistency, replication, evidentiary confidence, and decision/clinical utility. In the prioritized corpus, 11 reviews attained R2, 21 reached C2, 1 reached EC3, and 11 reached T3. 74 of 75 remained at CU0-CU1, and none made it to T4-T5. Certain wearable, imaging, and neuromuscular applications produced the strongest validation signals. Where biomarker specificity, candidate-gene heterogeneity, calibration, and external transportability were uncertain, translation was weaker. Sensitivity analyses restricted to recent, methods-rich, and meta-analytic reviews yielded the same general conclusion. Evidence of demonstrated decision impact has not advanced as quickly as measurement and prediction. Thus, independent replication, intended-use-specific validation, calibration, incremental value, and potential athlete-management outcomes should be given more weight in future research.