Development of a supportive care framework for breast cancer survivor's unmet needs: A modified Delphi study.

AIM: To establish a supportive care framework for addressing unmet needs among breast cancer survivors, providing practical guidance for healthcare providers to assess and manage these needs, ultimately enhancing the health outcomes and quality of life of breast cancer survivors. DESIGN: We conducted a two-round Delphi survey to gather expert opinions regarding the unmet needs supportive care framework for breast cancer survivors. METHODS: Initial framework identification and inquiry questionnaire creation was achieved via literature search and expert group discussions, which included 15 experts from nursing practice, clinical medicine, nursing management and nursing education was conducted using a Delphi survey. To establish consensus, a two-round Delphi poll was done, using criteria based on the mean (≥4.0), coefficient of variation (CV < 0.25) and percentage for entire score (≥20%). RESULTS: Experts reached a consensus, leading to six care modules, and 28 care entries: Tumour Detection Support (three care entries), Management of Complications of Antitumor Therapy (seven care entries), Healthy Lifestyle Management (five care entries), Sexual and Fertility Support (four care entries), Psychosocial Support (four care entries) and Resource and Linkage Support (five care entries). CONCLUSION: To address breast cancer survivors' unmet needs, a supportive framework was developed to actively enhance their health outcomes. However, further refinement and feasibility testing using mobile devices or artificial intelligence are required. IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE: This pioneering framework prioritises addressing unmet needs and equips healthcare providers to assess and manage these needs effectively, facilitating the implementation of programs aimed at improving the well-being of breast cancer survivors. REPORTING METHOD: This study was guided by a modified guideline for the Conducting and Reporting of Delphi Studies (CREDES) (Palliative Medicine, 31(8), 684, 2017). PATIENT OR PUBLIC CONTRIBUTION: No Patient or Public Contribution. TRIAL AND PROTOCOL REGISTRATION: The Delphi study methodology does not require registration.

Status quo of advanced cancer patients participating in shared decision-making in China: a mixed study.

PURPOSE: Patients with advanced cancer are usually willing to participate in shared decision-making (SDM), but in clinical practice, patient participation is easily ignored. This study aimed to analyze the current SDM status of advanced cancer patients and related factors. METHODS: In quantitative research, we administered a cross-sectional survey to 513 advanced cancer patients in 16 tertiary hospitals in China. A sociodemographic information questionnaire, the Control Preference Scale (CPS), and Perceived-Involvement in Care Scale (PICS) were used to analyze current SDM status and influencing factors. Our qualitative research was based on the Ottawa Decision Support Framework (ODSF), and 17 advanced cancer patients were interviewed to explore their perceptions of SDM. RESULTS: Our quantitative results show that patients' actual and expected decision-making participation differed; statistically significant influencing factors were age, insurance, and whether patients were worried about the therapeutic effects. We also found that dynamic decision-making mode changes, disease information acquisition, decision-making participation obstacles, and family members' roles affected patients' SDM through qualitative interviews. CONCLUSION: Advanced cancer patients' SDM in China is dominated by sharing and continuously fluctuates. Influenced by Chinese traditional culture, family members play an important role in SDM. In clinical work, we should pay attention to the dynamic changes in patients' participation in decision-making and the role of family members.

Unmet needs of 1210 Chinese breast cancer survivors and associated factors: a multicentre cross-sectional study.

BACKGROUND: Breast cancer survivors (BCSs) often have potential unmet needs. Identification of the specific needs of BCSs is very significant for medical service provision. This study aimed to (1) investigate the unmet needs and quality of life (QoL) of BCSs in China, (2) explore the diverse factors associated with their unmet needs, and (3) assess the association between their unmet needs and QoL. METHODS: A multicentre, cross-sectional survey was administered to 1210 Chinese BCSs. The Cancer Survivor Profile-Breast Cancer and the Functional Assessment of Cancer Therapy-Breast scale were administered to survivors who gave informed consent to participate. Data were analysed using t-test, ANOVA, multiple regression analysis, and Pearson correlations. RESULTS: The 1192 participants completed questionnaires (response rate 98.51%). Our study reveals that the most prevalent unmet needs were in the 'symptom burden domain'. The unmet needs of BCSs depend on eleven factors; age, time since diagnosis, education level, occupation, payment, family income status, stage of cancer, treatment, family history of cancer, pain, and physical activities. To ensure the provision of high-quality survivorship care and a high satisfaction level, more attention should be paid to actively identifying and addressing the unmet needs of BCSs. The problem areas identified in the Cancer Survivor Profile for breast cancer were negatively associated with all subscales of QoL except the health behaviour domain, with the correlation coefficient ranging from - 0.815 to - 0.011. CONCLUSION: Chinese BCSs exhibit a high demand for unmet needs in this study, and the most prevalent unmet needs were in the 'symptom burden domain'. There was a significant association between patients' unmet needs (as defined in the Cancer Survivor Profile for breast cancer) and QoL. Future research should focus on enhancements to survivorship or follow-up care to address unmet needs and further improve QoL.

Tunable coupled-cavity semiconductor laser based on a half-wave bow-tie coupler.

We propose and experimentally demonstrate a widely tunable coupled-cavity semiconductor laser based on a novel half-wave bow-tie coupler. The laser comprises two resonate cavities connected through the bow-tie coupler consisting of two back-to-back corner reflectors connected with a short waveguide, which produces a half-wave cross-coupling phase with an optimum coupling coefficient for achieving high mode selectivity. No complex grating or multiple epitaxial growth is needed. By tuning a single electrode at a fixed temperature, 29.6 nm wavelength tuning covering 38 channels with 100 GHz frequency spacing is achieved experimentally, with a side-mode suppression ratio of over 35 dB.

Pattern dependence in high-speed Q-modulated distributed feedback laser.

We investigate the pattern dependence in high speed Q-modulated distributed feedback laser based on its complete physical structure and material properties. The structure parameters of the gain section as well as the modulation and phase sections are all taken into account in the simulations based on an integrated traveling wave model. Using this model, we show that an example Q-modulated DFB laser can achieve an extinction ratio of 6.8dB with a jitter of 4.7ps and a peak intensity fluctuation of less than 15% for 40Gbps RZ modulation signal. The simulation method is proved very useful for the complex laser structure design and high speed performance optimization, as well as for providing physical insight of the operation mechanism.

Heat source modeling, penetration analysis and parametric optimization of super spray MAG welding.

Main drives, cutterheads and other critical components of tunnel shield machines require welding with thick plates that leave roots over 5 mm. Full penetration welds cannot be achieved by conventional Pulsed MAG welding methods. This article introduces Super Spray MAG Welding technology and investigates its penetrating regularities and mechanisms through high-speed camera images, finite element simulation, and microstructural analysis. An optimal welding procedure was generated using a combination of Genetic Algorithm and Back Propagation Neural Network. The data show that Super Spray MAG arc exhibits greater concentration and stability than traditional MAG arc, marking its strong qualities in emitting high-energy beams. The morphological solidification pattern of the molten pool closely matches the FEM simulation results of the composite Gaussian surface heat source model and peak linear attenuation Gaussian cylinder heat source. The welding current mainly affects the penetration of the weld, followed by the extension of the wire, and lastly the welding speed. Increasing the welding current can transition droplet transfer from globular to spray, as well as alter microstructure development and mechanical characteristics. Suggested parameters for penetrating the 5 mm root were put forward. The BPNN-GA model established can effectively predict weld formation, and points out the optimal welding parameters.

Research on named entity recognition method of marine natural products based on attention mechanism.

Marine natural product (MNP) entity property information is the basis of marine drug development, and this entity property information can be obtained from the original literature. However, the traditional methods require several manual annotations, the accuracy of the model is low and slow, and the problem of inconsistent lexical contexts cannot be solved well. In order to solve the aforementioned problems, this study proposes a named entity recognition method based on the attention mechanism, inflated convolutional neural network (IDCNN), and conditional random field (CRF), combining the attention mechanism that can use the lexicality of words to make attention-weighted mentions of the extracted features, the ability of the inflated convolutional neural network to parallelize operations and long- and short-term memory, and the excellent learning ability. A named entity recognition algorithm model is developed for the automatic recognition of entity information in the MNP domain literature. Experiments demonstrate that the proposed model can properly identify entity information from the unstructured chapter-level literature and outperform the control model in several metrics. In addition, we construct an unstructured text dataset related to MNPs from an open-source dataset, which can be used for the research and development of resource scarcity scenarios.

Piston Wear Detection and Feature Selection Based on Vibration Signals Using the Improved Spare Support Vector Machine for Axial Piston Pumps.

A piston wear fault is a major failure mode of axial piston pumps, which may decrease their volumetric efficiency and service life. Although fault detection based on machine learning theory can achieve high accuracy, the performance mainly depends on the detection model and feature selection. Feature selection in learning has recently emerged as a crucial issue. Therefore, piston wear detection and feature selection are essential and urgent. In this paper, we propose a vibration signal-based methodology using the improved spare support vector machine, which can integrate the feature selection into the piston wear detection learning process. Forty features are defined to capture the piston wear signature in the time domain, frequency domain, and time-frequency domain. The relevance and impact of sparsity in 40 features are illustrated through the single and multiple statistical feature analysis. Model performance is assessed and the sparse features are discovered. The maximum model testing and training accuracy are 97.50% and 96.60%, respectively. Spare features s10, s12, Ew(8), x7, Ee(5), and Ee(4) are selected and validated. Results show that the proposed methodology is applicable for piston wear detection and feature selection, with high model accuracy and good feature sparsity.

Breast Cancer Stigma Scale: A Reliable and Valid Stigma Measure for Patients With Breast Cancer.

Purpose: This study aims to develop and validate a stigma scale for Chinese patients with breast cancer. Methods: Patients admitted to the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, for breast cancer treatment participated in this study. Development of the Breast Cancer Stigma Scale involved the following procedures: literature review, interview, and applying a theoretical model to generate items; the Breast Cancer Stigma Scale's content validity was assessed by a Delphi study (n = 15) and feedback from patients with breast cancer (n = 10); exploratory factor analysis (n = 200) was used to assess the construct validity; convergent validity was assessed with the Social Impact Scale (n = 50); internal consistency Cronbach's α (n = 200), split-half reliability (n = 200), and test-retest reliability (N = 50) were used to identify the reliability of the scale. Results: The final version of the Breast Cancer Stigma Scale consisted of 15 items and showed positive correlations with the Social Impact Scale (ρ = 0.641, P < 0.001). Exploratory factor analysis (EFA) revealed four components of the Breast Cancer Stigma Scale: self-image impairment, social isolation, discrimination, and internalized stigma, which were strongly related to our perceived breast cancer stigma model and accounted for 69.443% of the total variance. Cronbach's α for the total scale was 0.86, and each subscale was 0.75-0.882. The test-retest reliability with intra-class correlation coefficients of the total scale was 0.947 (P < 0.001), and split-half reliability with intra-class correlation coefficients of the total scale was 0.911 (P < 0.001). The content validity index (CVI) was 0.73-1.0. Conclusion: The newly developed Breast Cancer Stigma Scale offers a valid and reliable instrument for assessing the perceived stigma of patients with breast cancer in clinical and research settings. It may be helpful for stigma prevention in China.

A case study on TBM cutterhead temperature monitoring and mud cake formation discrimination method.

The mud cake is easily formed during the tunnel boring machine (TBM) excavation in clay soils or rocks containing clay minerals. Mud cake will lead to soil disturbance of tunnel face, clogging cutterhead and even affect the construction efficiency and personnel safety. In this study, a discrimination method of mud cake formation based on cutterhead temperature was proposed. An online monitoring system was designed and installed on the slurry balance TBM. The results show that: (a) the cutterhead temperature data can be reliably detected and transmitted by the system; (b) in a tunneling cycle, the temperature at some positions of the cutterhead will increase first and then decrease; (c) during the field test, the temperature variation is around 2.5 °C under the normal condition, but the temperature variation will increase more than 50 °C due to the mud cake or geological change; (d) compared with the cooling rate, mud cake formation can be accurately discriminated.