GMC suspends doctor for false research claims
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ABSTRACT:
SUBMITTER: Dyer O
PROVIDER: S-EPMC1298881 | biostudies-literature | 2005 Dec
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe glucose-methanol-choline (GMC) superfamily is a large and functionally diverse family of oxidoreductases that share a common structural fold. Fungal members of this superfamily that are characterised and relevant for lignocellulose degradation include aryl-alcohol oxidoreductase, alcohol oxidase, cellobiose dehydrogenase, glucose oxidase, glucose dehydrogenase, pyranose dehydrogenase, and pyranose oxidase, which together form family AA3 of the auxiliary activities in the CAZy database of carbohydrate-active enzymes. Overall, little is known about the extant sequence space of these GMC oxidoreductases and their phylogenetic relations. Although some individual forms are well characterised, it is still unclear how they compare in respect of the complete enzyme class and, therefore, also how generalizable are their characteristics.ResultsTo improve the understanding of the GMC superfamily as a whole, we used sequence similarity networks to cluster large numbers of fungal GMC sequences and annotate them according to functionality. Subsequently, different members of the GMC superfamily were analysed in detail with regard to their sequences and phylogeny. This allowed us to define the currently characterised sequence space and show that complete clades of some enzymes have not been studied in any detail to date. Finally, we interpret our results from an evolutionary perspective, where we could show, for example, that pyranose dehydrogenase evolved from aryl-alcohol oxidoreductase after a change in substrate specificity and that the cytochrome domain of cellobiose dehydrogenase was regularly lost during evolution.ConclusionsThis study offers new insights into the sequence variation and phylogenetic relationships of fungal GMC/AA3 sequences. Certain clades of these GMC enzymes identified in our phylogenetic analyses are completely uncharacterised to date, and might include enzyme activities of varying specificities and/or activities that are hitherto unstudied.
| S-EPMC6509819 | biostudies-literature
Project description: Not available
| S-EPMC10630893 | biostudies-literature
Project description: Not available
| S-EPMC557258 | biostudies-literature
Project description:BackgroundFalse and misleading advertising for drugs can harm consumers and the healthcare system, and previous research has demonstrated that physician-targeted drug advertisements may be misleading. However, there is a dearth of research comparing consumer-targeted drug advertising to evidence to evaluate whether misleading or false information is being presented in these ads.ObjectiveTo compare claims in consumer-targeted television drug advertising to evidence, in order to evaluate the frequency of false or misleading television drug advertising targeted to consumers.DesignA content analysis of a cross-section of television advertisements for prescription and nonprescription drugs aired from 2008 through 2010. We analyzed commercial segments containing prescription and nonprescription drug advertisements randomly selected from the Vanderbilt Television News Archive, a census of national news broadcasts.Main measuresFor each advertisement, the most-emphasized claim in each ad was identified based on claim iteration, mode of communication, duration and placement. This claim was then compared to evidence by trained coders, and categorized as being objectively true, potentially misleading, or false. Potentially misleading claims omitted important information, exaggerated information, made lifestyle associations, or expressed opinions. False claims were factually false or unsubstantiated.Key resultsOf the most emphasized claims in prescription (n = 84) and nonprescription (n = 84) drug advertisements, 33 % were objectively true, 57 % were potentially misleading and 10 % were false. In prescription drug ads, there were more objectively true claims (43 %) and fewer false claims (2 %) than in nonprescription drug ads (23 % objectively true, 7 % false). There were similar numbers of potentially misleading claims in prescription (55 %) and nonprescription (61 %) drug ads.ConclusionsPotentially misleading claims are prevalent throughout consumer-targeted prescription and nonprescription drug advertising on television. These results are in conflict with proponents who argue the social value of drug advertising is found in informing consumers about drugs.
| S-EPMC3889958 | biostudies-literature
Project description:Ioannidis estimated that most published research findings are false, but he did not indicate when, if at all, potentially false research results may be considered as acceptable to society. We combined our two previously published models to calculate the probability above which research findings may become acceptable. A new model indicates that the probability above which research results should be accepted depends on the expected payback from the research (the benefits) and the inadvertent consequences (the harms). This probability may dramatically change depending on our willingness to tolerate error in accepting false research findings. Our acceptance of research findings changes as a function of what we call "acceptable regret," i.e., our tolerance of making a wrong decision in accepting the research hypothesis. We illustrate our findings by providing a new framework for early stopping rules in clinical research (i.e., when should we accept early findings from a clinical trial indicating the benefits as true?). Obtaining absolute "truth" in research is impossible, and so society has to decide when less-than-perfect results may become acceptable.
| S-EPMC1808081 | biostudies-literature
Project description:ObjectiveTo create and validate a methodology to assign a severity level to an episode of COVID-19 for retrospective analysis in claims data.Data sourceSecondary data obtained by license agreement from Optum provided claims records nationally for 19,761,754 persons, of which, 692,094 persons had COVID-19 in 2020.Study designThe World Health Organization (WHO) COVID-19 Progression Scale was used as a model to identify endpoints as measures of episode severity within claims data. Endpoints used included symptoms, respiratory status, progression to levels of treatment and mortality.Data collection/extraction methodsThe strategy for identification of cases relied upon the February 2020 guidance from the Centers for Disease Control and Prevention (CDC).Principal findingsA total of 709,846 persons (3.6%) met the criteria for one of the nine severity levels based on diagnosis codes with 692,094 having confirmatory diagnoses. The rates for each level varied considerably by age groups, with the older age groups reaching higher severity levels at a higher rate. Mean and median costs increased as severity level increased. Statistical validation of the severity scales revealed that the rates for each level varied considerably by age group, with the older ages reaching higher severity levels (p < 0.001). Other demographic factors such as race and ethnicity, geographic region, and comorbidity count had statistically significant associations with severity level of COVID-19.ConclusionA standardized severity scale for use with claims data will allow researchers to evaluate episodes so that analyses can be conducted on the processes of intervention, effectiveness, efficiencies, costs and outcomes related to COVID-19.
| S-EPMC10131339 | biostudies-literature
Project description:BackgroundThe GMC oxidoreductases comprise a large family of diverse FAD enzymes that share a homologous backbone. The relationship and origin of the GMC oxidoreductase genes, however, was unknown. Recent sequencing of entire genomes has allowed for the evolutionary analysis of the GMC oxidoreductase family.ResultsAlthough genes that encode enzyme families are rarely linked in higher eukaryotes, we discovered that the majority of the GMC oxidoreductase genes in the fruit fly (D. melanogaster), mosquito (A. gambiae), honeybee (A. mellifera), and flour beetle (T. castaneum) are located in a highly conserved cluster contained within a large intron of the flotillin-2 (Flo-2) gene. In contrast, the genomes of vertebrates and the nematode C. elegans contain few GMC genes and lack a GMC cluster, suggesting that the GMC cluster and the function of its resident genes are unique to insects or arthropods. We found that the development patterns of expression of the GMC cluster genes are highly complex. Among the GMC oxidoreductases located outside of the GMC gene cluster, the identities of two related enzymes, glucose dehydrogenase (GLD) and glucose oxidase (GOX), are known, and they play major roles in development and immunity. We have discovered that several additional GLD and GOX homologues exist in insects but are remotely similar to fungal GOX.ConclusionWe speculate that the GMC oxidoreductase cluster has been conserved to coordinately regulate these genes for a common developmental or physiological function related to ecdysteroid metabolism. Furthermore, we propose that the GMC gene cluster may be the birthplace of the insect GMC oxidoreductase genes. Through tandem duplication and divergence within the cluster, new GMC genes evolved. Some of the GMC genes have been retained in the cluster for hundreds of millions of years while others might have transposed to other regions of the genome. Consistent with this hypothesis, our analysis indicates that insect GOX and GLD arose from a different ancestral GMC gene than that of fungal GOX.
| S-EPMC1891103 | biostudies-literature
Project description: Not available
| S-EPMC1647372 | biostudies-literature
Project description:ObjectiveTo evaluate published algorithms for the identification of epilepsy cases in medical claims data using a unique linked dataset with both clinical and claims data.MethodsUsing data from a large, regional health delivery system, we identified all patients contributing biologic samples to the health system's Biobank (n = 36K). We identified all subjects with at least one diagnosis potentially consistent with epilepsy, for example, epilepsy, convulsions, syncope, or collapse, between 2014 and 2015, or who were seen at the epilepsy clinic (n = 1,217), plus a random sample of subjects with neither claims nor clinic visits (n = 435); we then performed a medical chart review in a random subsample of 1,377 to assess the epilepsy diagnosis status. Using the chart review as the reference standard, we evaluated the test characteristics of six published algorithms.ResultsThe best-performing algorithm used diagnostic and prescription drug data (sensitivity = 70%, 95% confidence interval [CI] 66-73%; specificity = 77%, 95% CI 73-81%; and area under the curve [AUC] = 0.73, 95%CI 0.71-0.76) when applied to patients age 18 years or older. Restricting the sample to adults aged 18-64 years resulted in a mild improvement in accuracy (AUC = 0.75,95%CI 0.73-0.78). Adding information about current antiepileptic drug use to the algorithm increased test performance (AUC = 0.78, 95%CI 0.76-0.80). Other algorithms varied in their included data types and performed worse.SignificanceCurrent approaches for identifying patients with epilepsy in insurance claims have important limitations when applied to the general population. Approaches incorporating a range of information, for example, diagnoses, treatments, and site of care/specialty of physician, improve the performance of identification and could be useful in epilepsy studies using large datasets.
| S-EPMC6592609 | biostudies-literature
Project description:Diagnostic screening models for the interpretation of null hypothesis significance test (NHST) results have been influential in highlighting the effect of selective publication on the reproducibility of the published literature, leading to John Ioannidis' much-cited claim that most published research findings are false. These models, however, are typically based on the assumption that hypotheses are dichotomously true or false, without considering that effect sizes for different hypotheses are not the same. To address this limitation, we develop a simulation model that overcomes this by modeling effect sizes explicitly using different continuous distributions, while retaining other aspects of previous models such as publication bias and the pursuit of statistical significance. Our results show that the combination of selective publication, bias, low statistical power and unlikely hypotheses consistently leads to high proportions of false positives, irrespective of the effect size distribution assumed. Using continuous effect sizes also allows us to evaluate the degree of effect size overestimation and prevalence of estimates with the wrong sign in the literature, showing that the same factors that drive false-positive results also lead to errors in estimating effect size direction and magnitude. Nevertheless, the relative influence of these factors on different metrics varies depending on the distribution assumed for effect sizes. The model is made available as an R ShinyApp interface, allowing one to explore features of the literature in various scenarios.
| S-EPMC9767354 | biostudies-literature