ObjectiveTo analyze the impact of the residence of patients with colorectal cancer (CRC) on surgical characteristics in the current version Database from Colorectal Cancer (DACCA). MethodsAccording to the established screening conditions, the patients with CRC were collected from the updated version of DACCA on January 23, 2023. The analysis indicators enrolled in this study included: the grouping indicator was residence, the surgical characteristic indicators included the surgical timing, surgical nature, expanded resection, intersphincteric resection (ISR) type, patient’s willingness of preserving the anus, and whether preserving the anus. The patients were categorized into three groups based on the residence: inside Chengdu City, outside Chengdu City within Sichuan Province, and outside Sichuan Province. The surgical characteristic indicators of patients with CRC from different residences were comparatively analyzed. ResultsA total of 6 832 analyzable data rows were enrolled. The results of statistical analysis revealed the following findings: there were no statistical differences in the surgery timing and surgery nature among the patients with colon cancer or rectal cancer from the different residences (Surgery timing: H=1.665, P=0.435; H=4.153, P=0.125. Surgery nature: χ2=1.586, P=0.453; χ2=0.990, P=0.610); For the patients with rectal cancer from the different residences, the distributions of the ISR type (H=0.514, P=0.773), patients’ willingness of preserving the anus (χ2=1.437, P=0.487), and whether preserving the anus (χ2=5.513, P=0.064) had no statistical differences. In addition, although there was no statistical difference in the distribution of expanded resection or not among the patients with rectal cancer in different residences (χ2=2.363, P=0.307), it was found that there was statistical difference in the distribution of enlarged resection or not among the patients with colon cancer in different residences (χ2=17.324, P＜0.001). ConclusionsFrom the data analysis in DACCA, there are not statistical differences in surgical characteristic indicators such as surgical timing, surgical nature, ISR type, patients’ willingness of preserving the anus, and whether preserving the anus among patients with colon or rectal cancer from different residences. However, the proportion of underwent expanded surgery in the colon cancer patients who from outside Sichuan Province as compared with inside Sichuan Province is relatively higher, this suggests that surgical difficulty is more difficult for the patients from outside Sichuan Province.
ObjectiveTo analyze the relationship between occupational type of patients with colorectal cancer (CRC) and decision-making and curative effect of neoadjuvant therapy in the current version of the Database from Colorectal Cancer (DACCA). MethodsThe eligible CRC patients were collected from June 29, 2022 updated DACCA according to the screening criteria, in which the data items analyzed included: gender, age, BMI, blood type, marriage, occupation, neoadjuvant therapy, symptomatic changes, imaging changes, and tumor regression grade (TRG), and the occupations were classified into the mental labour group, physical labour group, and the unemployed and resident groups according to the type of labour, then compared the decision-making and curative effect of neoadjuvant therapy among the 3 groups. ResultsA total of 2 415 eligible data were screened, of which 1 160 (48.0%) were the most in the manual labour group, followed by 877 (36.3%) in the unemployed and resident group, and finally 378 (15.7%) in the mental labour group. The proportion of those who did not use targeted drugs was higher in both patients ≤60 years old and >60 years old [75.6% (958/1 267) vs. 82.5% (947/1 148)], with both differences being statistically significant (P=0.004 and P=0.019), and among patients >60 years old, the different occupational types were associated with symptomatic changes and imaging changes after neoadjuvant therapy, with the highest number of both changes to partial remission [71.5% (161/225) vs. 66.7% (148/222)], both differences being statistically significant (P=0.001 and P=0.017). ConclusionThe analysis results of DACCA data reveal that the occupational type of CRC patients was associated with the choice of neoadjuvant therapy, and that different occupational types were associated with changes in curative effect before and after neoadjuvant therapy in CRC patients >60 years old, which needs to be further analysis for the reasons.
To promote the accessibility and application of guidelines, it is necessary to establish a professional guideline database to adapt to the rapid growth of TCM clinical practice guidelines. This study described the framework design, technology module, information management, and quality control of the clinical practice guideline database of traditional Chinese medicine (G-TCM). G-TCM had included 658 TCM clinical practice guidelines, which would provide a platform for clinicians, researchers, guideline makers (revision), and evaluators to quickly query and obtain clinical guideline information, and play a supporting role in promoting the standardization and accessibility of TCM clinical practice guidelines and better guiding clinical practice.
Objective To establish a Chinese clinical controlled trials database of neurology. Methods We identified relevant studies by electronic searching of CBMdisc from 1978 to April 2002, and the Library of Evidence-Based Medicine (Chinese). We also searched manually 11 journals and 4 conference proceedings relevant to neurological diseases. The studies included in this database should be controlled studies relevant to treatment on neurological disease, randomized or only controlled without randomization. Results 3 641 studies were included in this database. Conclusions The Chinese neurological trials database was established. This database will provide Chinese evidence on treatments of various neurological diseases. Studies in this database will also be included in the Cochrane Library to facilitate systematic reviewers.
ObjectiveTo analyze the relation between educational level of patients with colorectal cancer (CRC) and decision-making and curative effect of neoadjuvant therapy (NAT) in the current version of the Database from Colorectal Cancer (DACCA). MethodsThe eligible CRC patients were collected from June 29, 2022 updated DACCA according to the screening criteria and were assigned into 4 groups according to their educational level, namely, uneducated, primary educated, secondary educated, and tertiary educated. The differences in NAT decision-making, cancer marker change, symptomatic change, gross change, imaging change, and tumor regression grade (TRG) among the CRC patients with different educational levels were compared. ResultsA total of 2 816 data that met the screening criteria were collected, 138 of whom were uneducated, 777 of whom were primary educated, 1 414 of whom were secondary educated, and 487 of whom were tertiary educated. The analysis results revealed that the difference in the composition ratio of patients choosing NAT regimens by educational level was statistically significant (χ2=30.937, P<0.001), which was reflected that the composition ratio of choosing a simple chemotherapy regimen in the uneducated CRC patients was highest, while which of choosing combined targeted therapy regimen in the tertiary educated CRC patients was highest. In terms of treatment outcomes, the composition ratios of changes in cancer markers (H=4.795, P=0.187), symptoms (H=1.722, P=0.632), gross (H=2.524, P=0.471), imaging (H=2.843, P=0.416), and TRG (H=2.346, P=0.504) had no statistical differences. ConclusionsThrough data analysis in DACCA, it is found that the educational level of patients with CRC can affect the choice of NAT scheme. However, it is not found that the educational level is related to the changes in the curative effect of patients with CRC before and after NAT, and further analysis is needed to determine the reasons for this.
Objective To investigate the incidence and the correlative factors of diabetic retinopathy (DR) in patients with diabetes mellitus（DM）who lives in Beixinjing blocks, Shanghai. Methods Residents with DM were enrolled according to resident health archives. The data of disease history, visual acuity, eye disease and introcular pressure were collected by inquiry and examination. Photography of ocular fundus was used to confirm the diagnosis of DR. Results A total of 535 residents excepted the examination with the participating rate of 90.68%, in whom 146 (27.29%) were identified as with DR. The incidence of single and proliferative DR was 22.29% and 4.30%, respectively. Duration of DM was the independent risk factor of DR, while long duration of DM, accompanied with peripheral neuropathy and body mass index was the in-order independent factor of proliferative DR. Conclusions The incidence of DR is high in residents with DM. Monitoring DR progress in DM residents with risk factors is recommended. (Chin J Ocul Fundus Dis, 2006, 22: 31-34)
In order to fully explore the neural oscillatory coupling characteristics of patients with mild cognitive impairment (MCI), this paper analyzed and compared the strength of the coupling characteristics for 28 MCI patients and 21 normal subjects under six different-frequency combinations. The results showed that the difference in the global phase synchronization index of cross-frequency coupling under δ-θ rhythm combination was statistically significant in the MCI group compared with the normal control group (P = 0.025, d = 0.398). To further validate this coupling feature, this paper proposed an optimized convolutional neural network model that incorporated a time-frequency data enhancement module and batch normalization layers to prevent overfitting while enhancing the robustness of the model. Based on this optimized model, with the phase locking value matrix of δ-θ rhythm combination as the single input feature, the diagnostic accuracy of MCI patients was (95.49 ± 4.15)%, sensitivity and specificity were (93.71 ± 7.21)% and (97.50 ± 5.34)%, respectively. The results showed that the characteristics of the phase locking value matrix under the combination of δ-θ rhythms can adequately reflect the cognitive status of MCI patients, which is helpful to assist the diagnosis of MCI.
ObjectiveTo construct a demand model for electronic medical record (EMR) data quality in regards to the lifecycle in machine learning (ML)-based disease risk prediction, to guide the implementation of EMR data quality assessment. MethodsReferring to the lifecycle in ML-based predictive model, we explored the demand for EMR data quality. First, we summarized the key data activities involved in each task on predicting disease risk with ML through a literature review. Second, we mapped the data activities in each task to the associated requirements. Finally, we clustered those requirements into four dimensions. ResultsWe constructed a three-layer structured ring to represent the demand model for EMR data quality in ML-based disease risk prediction research. The inner layer shows the seven main tasks in ML-based predictive models: data collection, data preprocessing, feature representation, feature selection and extraction, model training, model evaluation and optimization, and model deployment. The middle layer is the key data activities in each task; and the outer layer represents four dimensions of data quality requirements: operability, completeness, accuracy, and timeliness. ConclusionThe proposed model can guide real-world EMR data governance, improve its quality management, and promote the generation of real-world evidence.
Sample size, mean and standard deviation are necessary when conducting meta-analysis for continuous outcomes. Advanced methods of data extraction were needed if the mean and the standard deviation couldn’t be obtained from a literature directly. Eight methods were introduced and two examples were given to illustrate how to apply the methods.
In computer-aided medical diagnosis, obtaining labeled medical image data is expensive, while there is a high demand for model interpretability. However, most deep learning models currently require a large amount of data and lack interpretability. To address these challenges, this paper proposes a novel data augmentation method for medical image segmentation. The uniqueness and advantages of this method lie in the utilization of gradient-weighted class activation mapping to extract data efficient features, which are then fused with the original image. Subsequently, a new channel weight feature extractor is constructed to learn the weights between different channels. This approach achieves non-destructive data augmentation effects, enhancing the model's performance, data efficiency, and interpretability. Applying the method of this paper to the Hyper-Kvasir dataset, the intersection over union (IoU) and Dice of the U-net were improved, respectively; and on the ISIC-Archive dataset, the IoU and Dice of the DeepLabV3+ were also improved respectively. Furthermore, even when the training data is reduced to 70 %, the proposed method can still achieve performance that is 95 % of that achieved with the entire dataset, indicating its good data efficiency. Moreover, the data-efficient features used in the method have interpretable information built-in, which enhances the interpretability of the model. The method has excellent universality, is plug-and-play, applicable to various segmentation methods, and does not require modification of the network structure, thus it is easy to integrate into existing medical image segmentation method, enhancing the convenience of future research and applications.