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Abstract Purpose This study aimed to evaluate the outcomes of Pelnac assisted by vacuum sealing drainage in managing severe avulsion injuries of the fingers. Methods This prospective study was conducted from May 2017 to September 2023, involving 12 consecutive patients with severe avulsion injuries of the fingers. Patients underwent single-stage or two-stage procedures employing Pelnac assisted by vacuum sealing drainage (VSD) for the management of severe avulsion injuries of the fingers. Post-operative follow-up was routinely scheduled, and aesthetic and functional outcomes, sensory recovery, complications were recorded. Results Twelve patients were included in the analysis, consisting of 9 males and 3 females, with a mean age of 30.6 ± 11.0 years. All patients presented with severe damage to tendons and deep tissues, as well as varying degrees of bone exposure and injury, with average defect area of 36.9 cm² (range, 11 to 180 cm²). At the final follow-up (mean, 14 months; range, 12 to 29 months), the average score on the Fingertip Injuries Outcome Score (FIOS) was 14.3 (SD 3.9, range 10 to 22), with 6 patients achieving excellent result, 4 classified as good and 2 as fair. Patients reported an average satisfaction score of 74.0 (SD 11.6; range 51 to 94) regarding the aesthetic appearance. The average score on the Vancouver Scar Scale score was 2.3 (SD 2.1, range 1.0 to 7.4). Ten patients reported normal or near-normal sensation, while 2 patients experienced slight sensory loss. The average score on the Disabilities of the Arm, Shoulder, and Hand (DASH) questionnaire was 13.2 (SD 8.5, range 0 to 47). No infections or hematomas were reported during hospitalization or after discharge. Conclusion Pelnac, assisted by VSD, proved to be an effective approach for managing severe avulsion injuries of the fingers. This approach can be considered as a viable alternative for addressing severe injuries or complex wound conditions.

Accurate and reliable precipitation forecasting is vital for effective water resource management and disaster mitigation, especially in geographically diverse and climatically complex regions like China. This study proposes an advanced methodology for medium- and long-term hydrological forecasting by integrating multiple machine learning models through a modified stacking ensemble strategy (MSES). We developed and compared five deterministic precipitation forecasting models, including elastic net regression (ENR), support vector regression, random forest, extreme gradient boosting, and light gradient boosting to provide forecasts with lead times ranging from 0 to 5 months at a spatial resolution of 0.5$^\circ$. The MSES was then evaluated against the traditional Bayesian model averaging (BMA) approach. Our comprehensive evaluation, based on deterministic forecasting metrics such as the anomaly correlation coefficient (ACC), mean squared skill score (MSSS), and Graded Precipitation Score (Pg), demonstrated the MSES outperformed individual models and the BMA method. The MSES achieved ACC scores between 0.6 and 0.9 for lead time (LT) $= 0$ month, with an average of around 0.8 for LT $= 2$ months. The MSSS for MSES was above 0.5 in more than half of the evaluations, and the Pg score was consistently above 80, indicating high accuracy in precipitation magnitude prediction. These findings highlight the promise of advanced machine learning strategies like MSES in improving the accuracy and robustness of precipitation forecasts, addressing critical needs in water resource management and disaster mitigation in China.