OBJECTIVES: To develop a locally deployed DeepSeek-powered closed-loop system for electronic nursing documentation quality control (QC) and evaluate its clinical efficacy through a multidimensional validation framework. MATERIALS AND METHODS: We implemented a three-dimensional (3D) QC framework (real-time, final, and vertical QC). A retrospective analysis of 556 electronic nursing records was conducted to evaluate pre- and postimplementation outcomes, with documentation accuracy and audit efficiency assessed via blinded nurse evaluations. RESULTS: After implementation, omission rates decreased from 7.19% to 1.79%, the prevalence of logical inconsistencies decreased from 9.35% to 0.72%, and the prevalence of timeliness errors decreased from 8.63% to 0%. The QC time per record decreased by 3.2-fold. Nurse satisfaction was evaluated using the Clinical Nursing Information System Effectiveness Evaluation Scale (Zhao Y, Gu Y, Zhang X, et al. Developed the clinical nursing information system effectiveness evaluation scale based on the new D&M model and conducted reliability and validity evaluation. Chin J Prae Nurs. 2020;36:544-550. https://doi.org/10.3760/cma.j.issn.1672-7088.2020.07.013), yielding a total score of 102.73 ± 3.25 out of a maximum 115 points. DISCUSSION: This study demonstrates that the Artificial Intelligence (AI)-powered closed-loop QC system significantly enhances documentation accuracy and workflow efficiency while ensuring data security. The 3D framework (real-time, final, and vertical QC) represents a paradigm shift from reactive to proactive quality governance in nursing practice. High nurse satisfaction (102.73/115) confirms clinical viability, offering a scalable model for intelligent health-care quality ecosystems. Future work should explore federated learning for multicenter deployment and regulatory frameworks for clinical AI. CONCLUSION: DeepSeek demonstrated robust efficacy in enhancing QC accuracy and workflow efficiency, with localized deployment ensuring data security. This system redefines nursing documentation management, heralding an era of "intelligent negative feedback" in health-care quality ecosystems.

In this paper, we consider the low-bit quantization problem of face recognition (FR) under the open-set protocol. Different from well explored low-bit quantization on closed-set image classification task, the open-set task is more sensitive to quantization errors (QEs). We redefine the QEs in angular space and disentangle it into class error and individual error. These two parts correspond to inter-class separability and intra-class compactness, respectively. Instead of eliminating the entire QEs, we propose the rotation consistent margin (RCM) loss to minimize the individual error, which is more essential to feature discriminative power. Extensive experiments on popular benchmark datasets such as MegaFace Challenge, Youtube Faces (YTF), Labeled Face in the Wild (LFW) and IJB-C show the superiority of proposed loss in low-bit FR quantization tasks.