Baseline patient characteristics and surgical and anesthetic datas. by Changxu Wang (17826805)

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Standard deviation of the difference between the RV-GLS value of the expert and trainees in four consecutive groups of 25 fetal heart clips. by Chantelle M. de Vet (16535257)

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LV-GLS agreement analysis between expert and trainees over consecutive quartiles of 25 measurements. by Chantelle M. de Vet (16535257)

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GLS analysis with 2D-STE software. by Chantelle M. de Vet (16535257)

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ICC of each trainee RV-GLS value compared with the expert in four consecutive groups of 25 fetal heart clips. by Chantelle M. de Vet (16535257)

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ICC of each trainee LV-GLS value compared with the expert in four consecutive groups of 25 fetal heart clips. by Chantelle M. de Vet (16535257)

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Mean of the difference between the RV-GLS value of the expert and trainees in four consecutive groups of 25 fetal heart clips. by Chantelle M. de Vet (16535257)

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Postoperative pain assessment indexes, nausea and vomiting frequency, and postoperative recovery of patients in two groups. by Changxu Wang (17826805)

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The flow chart of the study. by Changxu Wang (17826805)

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Mean of the difference between the LV-GLS value of the expert and trainees in four consecutive groups of 25 fetal heart clips. by Chantelle M. de Vet (16535257)

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S1 File - by Chantelle M. de Vet (16535257)

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Comparison of postoperative NLR, PLR, and SII between the two groups. by Changxu Wang (17826805)

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Univariate analysis of hospitalized patients. by Jie Zhao (49409)

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Univariate analysis of ICU patients. by Jie Zhao (49409)

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Clinical baseline data after PSM. by Jie Zhao (49409)

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International Classification of Diseases Codes. by Jie Zhao (49409)

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Baseline data of clinical ICU patients. by Jie Zhao (49409)

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PCA-CGAN model parameter settings. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”

MIT-BIH dataset proportion analysis chart. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”

Wavelet transform preprocessing results. by Chao Tang (10925)

“…Experiments demonstrate that PCA-CGAN not only achieves stable convergence on a large-scale heterogeneous dataset comprising 43 patients for the first time but also resolves the “dilution effect” problem in data augmentation, avoiding the asymmetric phenomenon where Precision increases while Recall decreases. After data augmentation, the ResNet model’s average F1 score improved significantly, with particularly outstanding performance on rare categories such as atrial premature beats, far surpassing traditional methods like SigCWGAN and TD-GAN. …”