Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Biology of reproduction
Noninvasive pre-transfer identification of women with recurrent implantation failure using a plasma microRNA-based machine learning model
Recurrent implantation failure (RIF) refers to patients who have undergone at least three implantation failures with several high-quality embryos. Identifying patients with RIF in advance is crucial for effective clinical managements. However, uterine biopsy, the primary research approach, is not only invasive but also poses a risk to the integrity of the endometrial microenvironment. Here, we provided a novel, safe, and immediate methodology for RIF populations identification through plasma microRNA (miRNA) profiles. Peripheral blood samples of 163 women were collected and sequenced to quantify the relative expression levels of plasma miRNAs. Through a comparative analysis of the temporal expression between patients with RIF and those with successful embryo implantation during the peri-implantation period, we determined that the RIF-associated miRNA signature was established at the early stage of the window of implantation. By global screening, we identified 217 miRNAs that were differentially expressed between groups on the ovulation day (D0), using a threshold of |fold change| ≥ 1.5. Through refining selection criteria, 10 eligible miRNAs were ultimately selected and further validated through real-time quantitative polymerase chain reaction. These miRNAs were employed to construct a logistic regression-based RIF model. The model demonstrated robust performance in the validation and test cohorts, with area under the curve of 0.947 and 0.912, respectively. We also demonstrated that miRNAs maintain relative stability within a specific phase, making them suitable as clinical biomarkers. This study provides a noninvasive methodology for identifying women with RIF and suggests a potential role in predicting RIF risk before embryo transfer.
16 July 2026
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A Randomized Trial of Interval Likelihood Ratios for Clinical Decision-Making
BACKGROUND: Dichotomized reporting of diagnostic test results may oversimplify test characteristics. Interval likelihood ratios (ILRs), test characteristics across a range of values, offer a nuanced alternative, but their impact on decision-making is uncertain. We assessed whether ILRs improve clinical decisions compared to two traditional formats: single-threshold and binary test characteristics. METHODS: We conducted a multicenter randomized trial of internal medicine residents, attending physicians, and advanced practice providers. Participants were presented with two fictional vignettes (cases involving pulmonary embolism diagnosis and heart failure treatment), each with a clearly correct response centered on a key diagnostic test result. The vignettes were identical except for participants' random assignment to one of three test characteristic presentations: (1) single threshold, (2) binary test characteristics, or (3) ILRs. We compared the correct management decision rates between the ILR and threshold groups and between the ILR and binary groups. Secondary objectives included comparing threshold and binary groups and subgroup analyses by case vignette. RESULTS: The trial enrolled 611 participants. The correct decision rate was 93.4% in the ILR group compared with 82.2% in the threshold group (difference of 11.2 percentage points; 95% confidence interval [CI], 6.9-15.6, P<0.001) and compared with 75.1% in the binary group (difference of 18.3 percentage points; 95% CI, 13.5-23.1, P<0.001). In the pulmonary embolism case, the correct response rate was 91.9% in the ILR group, 70.9% in the threshold group, and 64.7% in the binary group. In the heart failure case, the correct response rate was 94.9% in the ILR group, 93.4% in the threshold group, and 85.6% in the binary group. CONCLUSIONS: Presenting diagnostic test information using ILRs improved vignette-based clinical decisions. The magnitude of improvement appeared to vary by case vignette.
24 June 2026
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