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Ma Yuan Sajeev Gautam VanderWeele Tyler J. Viswanathan Anand Sigurdsson Sigurdur Eiriksdottir Gudny Aspelund Thor Betensky Rebecca A. Grodstein Francine Hofman Albert Gudnason Vilmundur Launer Lenore Blacker Deborah 《European journal of epidemiology》2022,37(6):591-601
European Journal of Epidemiology - The apolipoprotein E allele 4 (APOE-ε4) is established as a major genetic risk factor for cognitive decline and late-onset Alzheimer’s disease.... 相似文献
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Jennifer VanderWeele Teresa Pollack Diana Johnson Oakes Colleen Smyrniotis Vidhya Illuri Priyathama Vellanki Kevin OLeary Jane Holl Grazia Aleppo Mark E. Molitch Amisha Wallia 《Journal of diabetes and its complications》2018,32(7):650-654
Aims
This study validated enterprise data warehouse (EDW) data for a cohort of hospitalized patients with a primary diagnosis of diabetic ketoacidosis (DKA).Methods
247 patients with 319 admissions for DKA (ICD-9 code 250.12, 250.13, or 250.xx with biochemical criteria for DKA) were admitted to Northwestern Memorial Hospital from 1/1/2010 to 9/1/2013. Validation was performed by electronic medical record (EMR) review of 10% of admissions (N?=?32). Classification of diabetes type (Type 1 vs. Type 2) and DKA clinical status were compared between the EMR review and EDW data.Results
Key findings included incorrect classification of diabetes type in 5 of 32 (16%) admissions and indeterminable classification in 5 admissions. DKA was not present, based on the review, in 11 of 32 (34%) admissions. DKA was not present, based on biochemical criteria, in 15 of 32 (47%) admissions.Conclusions
This study found that EDW data have substantial errors. Some discrepancies can be addressed by refining the EDW query code, while others, related to diabetes classification and DKA diagnosis, cannot be corrected without improving clinical coding accuracy, consistency of medical record documentation, or EMR design. These results support the need for comprehensive validation of data for complex clinical populations obtained through data repositories such as the EDW. 相似文献6.
We provide two simple metrics that could be reported routinely in random-effects meta-analyses to convey evidence strength for scientifically meaningful effects under effect heterogeneity (ie, a nonzero estimated variance of the true effect distribution). First, given a chosen threshold of meaningful effect size, meta-analyses could report the estimated proportion of true effect sizes above this threshold. Second, meta-analyses could estimate the proportion of effect sizes below a second, possibly symmetric, threshold in the opposite direction from the estimated mean. These metrics could help identify if (1) there are few effects of scientifically meaningful size despite a “statistically significant” pooled point estimate, (2) there are some large effects despite an apparently null point estimate, or (3) strong effects in the direction opposite the pooled estimate also regularly occur (and thus, potential effect modifiers should be examined). These metrics should be presented with confidence intervals, which can be obtained analytically or, under weaker assumptions, using bias-corrected and accelerated bootstrapping. Additionally, these metrics inform relative comparison of evidence strength across related meta-analyses. We illustrate with applied examples and provide an R function to compute the metrics and confidence intervals. 相似文献
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Various concepts of interaction are reconsidered in light of a sufficient-component-cause framework. Conditions and statistical tests are derived for the presence of synergism within sufficient causes. The conditions derived are sufficient but not necessary for the presence of synergism. In the context of monotonic effects, the conditions derived are closely related to effect modification on the risk difference scale; however, this is not the case without the assumption of monotonic effects. 相似文献
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VanderWeele TJ Vandenbroucke JP Tchetgen EJ Robins JM 《Epidemiology (Cambridge, Mass.)》2012,23(2):285-292
In this paper, we discuss relationships between causal interactions within the counterfactual framework and interference in which the exposure of one person may affect the outcomes of another. We show that the empirical tests for causal interactions can, in fact, all be adapted to empirical tests for particular forms of interference. In the context of interference, by recoding the response as some function of the outcomes of the various persons within a cluster, a wide range of different forms of interference can potentially be detected. The correspondence between causal interactions and forms of interference extends to encompass n-way causal interactions, interference between n persons within a cluster, and multivalued exposures. The theory for causal interactions provides a complete conceptual apparatus for assessing interference as well. The results are illustrated using data from a hypothetical vaccine trial to reason about specific forms of interference and spillover effects that may be present in this vaccine setting. We discuss the implications of this correspondence for our conceptualizations of interaction and for application to vaccine trials and many other settings in which spillover effects may be present. 相似文献
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VanderWeele TJ Asomaning K Tchetgen Tchetgen EJ Han Y Spitz MR Shete S Wu X Gaborieau V Wang Y McLaughlin J Hung RJ Brennan P Amos CI Christiani DC Lin X 《American journal of epidemiology》2012,175(10):1013-1020
Genome-wide association studies have identified variants on chromosome 15q25.1 that increase the risks of both lung cancer and nicotine dependence and associated smoking behavior. However, there remains debate as to whether the association with lung cancer is direct or is mediated by pathways related to smoking behavior. Here, the authors apply a novel method for mediation analysis, allowing for gene-environment interaction, to a lung cancer case-control study (1992-2004) conducted at Massachusetts General Hospital using 2 single nucleotide polymorphisms, rs8034191 and rs1051730, on 15q25.1. The results are validated using data from 3 other lung cancer studies. Tests for additive interaction (P = 2 × 10(-10) and P = 1 × 10(-9)) and multiplicative interaction (P = 0.01 and P = 0.01) were significant. Pooled analyses yielded a direct-effect odds ratio of 1.26 (95% confidence interval (CI): 1.19, 1.33; P = 2 × 10(-15)) for rs8034191 and an indirect-effect odds ratio of 1.01 (95% CI: 1.00, 1.01; P = 0.09); the proportion of increased risk mediated by smoking was 3.2%. For rs1051730, direct- and indirect-effect odds ratios were 1.26 (95% CI: 1.19, 1.33; P = 1 × 10(-15)) and 1.00 (95% CI: 0.99, 1.01; P = 0.22), respectively, with a proportion mediated of 2.3%. Adjustment for measurement error in smoking behavior allowing up to 75% measurement error increased the proportions mediated to 12.5% and 9.2%, respectively. These analyses indicate that the association of the variants with lung cancer operates primarily through other pathways. 相似文献