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11.
Kara S. Tanaka MD Veronica R. Andaya BA Steven W. Thorpe MD Kenneth R. Gundle MD James B. Hayden MD Yee-Cheen Duong MD Raffi S. Avedian MD David G. Mohler MD Lee J. Morse MD Melissa N. Zimel MD Richard J. O'Donnell MD Andrew Fang MD Robert Lor Randall MD Tina H. Tran BS Christin New BA Rosanna L. Wustrack MD other members of Study Group FORCE 《Journal of surgical oncology》2023,127(1):148-158
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Boffa Joseph W. Tock Jamie L. Morabito Danielle M. Schmidt Norman B. 《Cognitive therapy and research》2022,46(5):1016-1029
Cognitive Therapy and Research - Despite interest in psychological inflexibility as a marker of suicide risk, no measure of psychological inflexibility specific to SI exists. The present study... 相似文献
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Victoria L. Parker Matthew C. Winter John A. Tidy Barry W. Hancock Julia E. Palmer Naveed Sarwar Baljeet Kaur Katie McDonald Xianne Aguiar Kamaljit Singh Nick Unsworth Imran Jabbar Allan A. Pacey Robert F. Harrison Michael J. Seckl 《International journal of cancer. Journal international du cancer》2023,152(5):986-997
Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus. 相似文献
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Guo-Quan Shi Huajun Zhu & Zhen-Guo Yan 《Communications In Computational Physics》2022,31(4):1215-1241
A priori subcell limiting approach is developed for high-order flux reconstruction/correction procedure via reconstruction (FR/CPR) methods on two-dimensional unstructured quadrilateral meshes. Firstly, a modified indicator based on
modal energy coefficients is proposed to detect troubled cells, where discontinuities
exist. Then, troubled cells are decomposed into nonuniform subcells and each subcell has one solution point. A second-order finite difference shock-capturing scheme
based on nonuniform nonlinear weighted (NNW) interpolation is constructed to perform the calculation on troubled cells while smooth cells are calculated by the CPR
method. Numerical investigations show that the proposed subcell limiting strategy on
unstructured quadrilateral meshes is robust in shock-capturing. 相似文献
17.
Phoebe Hammer Kevin White Stephanie Mengden Vessy Korcheva Philipp W. Raess 《Journal of cutaneous pathology》2019,46(5):343-346
Cutaneous leiomyomas are rare benign smooth‐muscle tumors. These lesions are distinguished based on their cell of origin and are subclassified as pilar leiomyoma, angioleiomyoma, and genital‐type leiomyoma. Nipple leiomyoma is the least common genital‐type leiomyoma, arising from the dartoic muscle cell of the nipple. Histologic examination of the lesion is necessary for definitive diagnosis, and these uncommon tumors can pose a diagnostic challenge. We describe herein a series of six nipple leiomyomas with a spectrum of histologic appearances. 相似文献
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Wang-Shu Zhu Si-Ya Shi Ze-Hong Yang Chao Song Jun Shen 《World journal of gastroenterology : WJG》2020,26(11):1208-1220
BACKGROUND Postoperative liver failure is the most severe complication in cirrhotic patients with hepatocellular carcinoma(HCC) after major hepatectomy. Current available clinical indexes predicting postoperative residual liver function are not sufficiently accurate.AIM To determine a radiomics model based on preoperative gadoxetic acid-enhanced magnetic resonance imaging for predicting liver failure in cirrhotic patients with HCC after major hepatectomy.METHODS For this retrospective study, a radiomics-based model was developed based on preoperative hepatobiliary phase gadoxetic acid-enhanced magnetic resonance images in 101 patients with HCC between June 2012 and June 2018. Sixty-one radiomic features were extracted from hepatobiliary phase images and selected by the least absolute shrinkage and selection operator method to construct a radiomics signature. A clinical prediction model, and radiomics-based model incorporating significant clinical indexes and radiomics signature were built using multivariable logistic regression analysis. The integrated radiomics-based model was presented as a radiomics nomogram. The performances of clinical prediction model, radiomics signature, and radiomics-based model for predicting post-operative liver failure were determined using receiver operating characteristics curve, calibration curve, and decision curve analyses.RESULTS Five radiomics features from hepatobiliary phase images were selected to construct the radiomics signature. The clinical prediction model, radiomics signature, and radiomics-based model incorporating indocyanine green clearance rate at 15 min and radiomics signature showed favorable performance for predicting postoperative liver failure(area under the curve: 0.809-0.894). The radiomics-based model achieved the highest performance for predicting liver failure(area under the curve: 0.894; 95%CI: 0.823-0.964). The integrated discrimination improvement analysis showed a significant improvement in the accuracy of liver failure prediction when radiomics signature was added to the clinical prediction model(integrated discrimination improvement = 0.117, P =0.002). The calibration curve and an insignificant Hosmer-Lemeshow test statistic(P = 0.841) demonstrated good calibration of the radiomics-based model. The decision curve analysis showed that patients would benefit more from a radiomics-based prediction model than from a clinical prediction model and radiomics signature alone.CONCLUSION A radiomics-based model of preoperative gadoxetic acid–enhanced MRI can be used to predict liver failure in cirrhotic patients with HCC after major hepatectomy. 相似文献