Correction: Predictive models of recurrent implantation failure in patients receiving ART treatment based on clinical features and routine laboratory data.

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This article is a correction to a previous paper, updating specific statistical notation, figure captions, and author affiliations regarding predictive models for recurrent implantation failure in ART patients.

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1 caption and the author’s unit. The heading Correlations of clinical characteristics and laboratory indicators with RIF outcomes, in this part of the results “95% CI, 0.870 to 0.929” was modified to“95% CI, 0.870 ∼ 0.929” . Reproductive Biology and Endocrinology †Qunying Fang, Zonghui Qiao and Lei Luo contributed equally to this work. The online version of the original article can be found at https://doi. org/10.1186/s12958-024-01203-z. *Correspondence: Lu Zong [email protected] Xian-hong Tong [email protected] Li-min Wu [email protected] 1Center for Reproduction and Genetics, Division of Life Sciences and Medicine, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei 230026, Anhui, P . R. China 2University of Science and Technology of China, Hefei 230026, Anhui, P . R. China Correction: Predictive models of recurrent implantation failure in patients receiving ART treatment based on clinical features and routine laboratory data Qunying Fang1,2†, Zonghui Qiao1†, Lei Luo1†, Shun Bai1, Min Chen1,2, Xiangjun Zhang1,2, Lu Zong1*, Xian-hong Tong1* and Li-min Wu1* Page 2 of 2 Fang et al. Reproductive Biology and Endocrinology (2024) 22:45

References

1. Fang Q, Qiao Z, Luo L, et al. Predictive models of recurrent implantation fail- ure in patients receiving ART treatment based on clinical features and routine laboratory data. Reprod Biol Endocrinol. 2024;22:32. https://doi.org/10.1186/ s12958-024-01203-z. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Table 1 General clinical data (Corrected) Control RIF t/X2 P (n = 4750) (n = 462) Female Age (years) 31.00 ± 3.66 33.30 ± 4.14 6.07 0 Duration of infertility (years) 2.70 ± 2.04 4.06 ± 2.79 5.721 0 BMI (kg/m2) 22.92 ± 3.13 22.42 ± 3.02 1.677 0.094 Type of infertility 29.408 0 Primary infertility 2590(54.53) 191(41.34) Secondary infertility 2160(45.47) 271(58.66) Diagnosis of infertility Tubal factor 3220(67.79) 272(58.87) 15.135 0 PCOS 513(10.08) 51(11.04) 0.25 0.875 DOR 290(6.11) 91(19.70) 84.697 0 Other ovarian factors 105(2.21) 7(1.52) 0.968 0.325 Ems 430(9.05) 76(16.45) 26.286 0 Number of induced abortions 130.388 0 0 3315(69.79) 222(48.05) 1 1050(22.11) 137(29.65) ≥2 385(8.10) 103(22.30) Male Age (years)(X ± S) 31.98 ± 4.37 34.56 ± 4.72 5.821 0 Type of infertility 13.272 0 Primary infertility 2600(54.74) 212(45.89) Secondary infertility 2150(45.26) 250(54.11) Teratozoospermia 175(3.68) 20(4.33) 0.486 0.486 Sperm quality 192.875 0 Normal 2840(59.79) 136(29.44) Mild or moderate asthenospermia 1025(21.58) 218(47.19) Severe asthenospermia 320(6.74) 49(10.61) Others 565(11.89) 59(12.76) The data are presented as the mean ± SD or % (n). The data were analysed by ANOVA or the chi-square test and Fisher’s exact test BMI, body mass index; PCOS, polycystic ovarian syndrome; DOR, diminished ovarian reserve; EMs, endometriosis

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