Text-Based vs. Graphical Information Formats in Sepsis Prevention and Early Detection: A Randomized Controlled Trial on Informed Choice.
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ABSTRACT:
SUBMITTER: Debbeler LJ
PROVIDER: S-EPMC9267388 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Tamoxifen and raloxifene are chemopreventive drugs that can reduce women’s relative risk of primary breast cancer by 50%; however, most women eligible for these drugs have chosen not to take them. The reasons for low uptake may be related to women’s knowledge or attitudes towards the drugs. We aimed to examine the impact of an online breast cancer chemoprevention decision aid (DA) on informed intentions and decisions of women at high risk of breast cancer.We conducted a randomized clinical trial, assessing the effect of a DA about breast cancer chemoprevention on informed choices about chemoprevention. Women (n = 585), 46- to 74-years old old, completed online baseline, post-test, and three-month follow-up questionnaires. Participants were randomly assigned to either an intervention group, a standard control group that answered questions about chemoprevention at baseline, or a three-month control group that did not answer questions about chemoprevention at baseline. The main outcome measures were whether women’s intentions and decisions regarding chemoprevention drugs were informed, and whether women who viewed the DA were more likely to make informed decisions than women who did not view the DA, using a dichotomous composite variable ‘informed choice’ (yes/no) to classify informed decisions as those reflecting sufficient knowledge and concordance between a woman’s decision and relevant attitudes.Analyses showed that more intervention than standard control participants (52.7% versus 5.9%) made informed decisions at post-test, P <0.001. At the three-month follow-up, differences in rates of informed choice between intervention (16.9%) and both control groups (11.8% and 8.0%) were statistically non-significant, P = 0.067.The DA increased informed decision making about breast cancer chemoprevention, although the impact on knowledge diminished over time. This study was not designed to determine how much knowledge decision makers must retain over time. Examining informed decisions increases understanding of the impact of DAs. A standard for defining and measuring sufficient knowledge for informed decisions is needed.ClinicalTrials.gov: NCT00967824
| S-EPMC3978729 | biostudies-literature
Project description:Text formats are common in bioinformatics, as they allow for editing and filtering using standard tools, as well as, since text formats are often human readable, manual inspection and evaluation of the data. Bioinformatics is a rapidly evolving field, hence, new techniques, new software tools, new kinds of data often require the definition of new formats. Often new formats are not formally described in a standard or specification document. Although software libraries are available for accessing the most common formats, writing parsers for text formats, for which no library is currently available, is a very common though tedious task, utilized by many researchers in the field. This manuscript presents the open source software library and toolset TextFormats (available at https://github.com/ggonnella/textformats), which aims at simplifying the definition and parsing of text formats. Formats specifications are written in a simple data description format using an interactive wizard. Automatic generation of data examples and automatic testing of specifications allow for checking for correctness. Given the specification for a text format, TextFormats allows parsing and writing data in that format, using several programming languages (Nim, Python, C/C++) or the provided command line and graphical user interface tools. Although designed as a general purpose software, the main target application field, for the above mentioned reasons, is expected to be in bioinformatics: Thus, the specifications of several common existing bioinformatics formats are included.
| S-EPMC9135226 | biostudies-literature
Project description:The primary objective of this study is to evaluate the impact of a theoretically grounded video that includes information about CRC screening modality choices on CRC screening rates and time adherent to CRC guidelines.
| 2405219 | ecrin-mdr-crc
Project description:Objective:To examine the effects of graphical formats and age on consumers' comprehension and perceptions of the use of self-monitoring test results. Methods:Participants (36 older and 36 young adults) were required to perform verbatim comprehension and value interpretation tasks with hypothetical self-monitoring test results. The test results were randomly presented by four reference range number lines: basic, color enhanced, color/text enhanced, and personalized information enhanced formats. We measured participants' task performance and eye movement data during task completion, and their perceptions and preference of the graphical formats. Results:The 4 graphical formats yielded comparable task performance, while text/color and personalized information enhanced formats were believed to be easier and more useful in information comprehension, and led to increased confidence in correct comprehension of test results, compared with other formats (all p's < .05). Perceived health risk increased as the formats applied more information cues (p = .008). There were age differences in task performance and visual attention (all p's < .01), while young and older adults had similar perceptions for the 4 formats. Personalized information enhanced format was preferred by both groups. Conclusions:Text/color and personalized information cues appear to be useful for comprehending test results. Future work can be directed to improve the design of graphical formats especially for older adults, and to assess the formats in clinical settings.
| S-EPMC7646882 | biostudies-literature
Project description:False information detection can detect false information in social media and reduce its negative impact on society. With the development of multimedia, the multimodal content contained in false information is increasing, so it is important to use multimodal features to detect false information. This paper mainly uses information from two modalities, text and image. The features extracted by the backbone network are not further processed in the previous work, and the problems of noise and information loss in the process of fusing multimodal features are ignored. This paper proposes a false information detection method based on Text-CNN and SE modules. We use Text-CNN to process the text and image features extracted by BERT and Swin-transformer to enhance the quality of the features. In addition, we use the modified SE module to fuse text and image features and reduce the noise in the fusion process. Meanwhile, we draw on the idea of residual networks to reduce information loss in the fusion process by concatenating the original features with the fused features. Our model improves accuracy by 6.5% and 2.0% on the Weibo dataset and Twitter dataset compared to the attention based multimodal factorized bilinear pooling. The comparative experimental results show that the proposed model can improve the accuracy of false information detection. The results of ablation experiments further demonstrate the effectiveness of each module in our model.
| S-EPMC9683577 | biostudies-literature
Project description:ObjectiveTo determine the effects of using unstructured clinical text in machine learning (ML) for prediction, early detection, and identification of sepsis.Materials and methodsPubMed, Scopus, ACM DL, dblp, and IEEE Xplore databases were searched. Articles utilizing clinical text for ML or natural language processing (NLP) to detect, identify, recognize, diagnose, or predict the onset, development, progress, or prognosis of systemic inflammatory response syndrome, sepsis, severe sepsis, or septic shock were included. Sepsis definition, dataset, types of data, ML models, NLP techniques, and evaluation metrics were extracted.ResultsThe clinical text used in models include narrative notes written by nurses, physicians, and specialists in varying situations. This is often combined with common structured data such as demographics, vital signs, laboratory data, and medications. Area under the receiver operating characteristic curve (AUC) comparison of ML methods showed that utilizing both text and structured data predicts sepsis earlier and more accurately than structured data alone. No meta-analysis was performed because of incomparable measurements among the 9 included studies.DiscussionStudies focused on sepsis identification or early detection before onset; no studies used patient histories beyond the current episode of care to predict sepsis. Sepsis definition affects reporting methods, outcomes, and results. Many methods rely on continuous vital sign measurements in intensive care, making them not easily transferable to general ward units.ConclusionsApproaches were heterogeneous, but studies showed that utilizing both unstructured text and structured data in ML can improve identification and early detection of sepsis.
| S-EPMC8800516 | biostudies-literature
Project description:Based on the framework of testlet models, the current study suggests the Bayesian random block item response theory (BRB IRT) model to fit forced-choice formats where an item block is composed of three or more items. To account for local dependence among items within a block, the BRB IRT model incorporated a random block effect into the response function and used a Markov Chain Monte Carlo procedure for simultaneous estimation of item and trait parameters. The simulation results demonstrated that the BRB IRT model performed well for the estimation of item and trait parameters and for screening those with relatively low scores on target traits. As found in the literature, the composition of item blocks was crucial for model performance; negatively keyed items were required for item blocks. The empirical application showed the performance of the BRB IRT model was equivalent to that of the Thurstonian IRT model. The potential advantage of the BRB IRT model as a base for more complex measurement models was also demonstrated by incorporating gender as a covariate into the BRB IRT model to explain response probabilities. Recommendations for the adoption of forced-choice formats were provided along with the discussion about using negatively keyed items.
| S-EPMC7221495 | biostudies-literature
Project description:Here we show that Drosophila sechellia—a specialist on the fruit of Morinda citrifolia that recently diverged from its generalist sister-species, D. simulans—has rapidly accumulated loss-of-function alleles and reduced gene expression at genes affecting olfaction, detoxification, and metabolism. While D. sechellia increases expression of genes involved with oogenesis and fatty acid metabolism when on its host, many more genes show reduced expression in D. sechellia. For several functionally related genes, this decrease in expression is associated with loss-of-function alleles. The rapid accumulation of these alleles potentially affected D. sechellia’s initial adaptation to M. citrifolia, likely contributes to D. sechellia’s poor competitive ability off of its host, and increases ecological isolation between D. sechellia and its sister species. Keywords: comparative hybridization, gene expression
2008-11-25 | GSE13723 | GEO
Project description:BACKGROUND:The recent rise in popularity and scale of social networking services (SNSs) has resulted in an increasing need for SNS-based information extraction systems. A popular application of SNS data is health surveillance for predicting an outbreak of epidemics by detecting diseases from text messages posted on SNS platforms. Such applications share the following logic: they incorporate SNS users as social sensors. These social sensor-based approaches also share a common problem: SNS-based surveillance are much more reliable if sufficient numbers of users are active, and small or inactive populations produce inconsistent results. OBJECTIVE:This study proposes a novel approach to estimate the trend of patient numbers using indirect information covering both urban areas and rural areas within the posts. METHODS:We presented a TRAP model by embedding both direct information and indirect information. A collection of tweets spanning 3 years (7 million influenza-related tweets in Japanese) was used to evaluate the model. Both direct information and indirect information that mention other places were used. As indirect information is less reliable (too noisy or too old) than direct information, the indirect information data were not used directly and were considered as inhibiting direct information. For example, when indirect information appeared often, it was considered as signifying that everyone already had a known disease, leading to a small amount of direct information. RESULTS:The estimation performance of our approach was evaluated using the correlation coefficient between the number of influenza cases as the gold standard values and the estimated values by the proposed models. The results revealed that the baseline model (BASELINE+NLP) shows .36 and that the proposed model (TRAP+NLP) improved the accuracy (.70, +.34 points). CONCLUSIONS:The proposed approach by which the indirect information inhibits direct information exhibited improved estimation performance not only in rural cities but also in urban cities, which demonstrated the effectiveness of the proposed method consisting of a TRAP model and natural language processing (NLP) classification.
| S-EPMC6231889 | biostudies-other
Project description:Here we show that Drosophila sechelliaM-bM-^@M-^Ta specialist on the fruit of Morinda citrifolia that recently diverged from its generalist sister-species, D. simulansM-bM-^@M-^Thas rapidly accumulated loss-of-function alleles and reduced gene expression at genes affecting olfaction, detoxification, and metabolism. While D. sechellia increases expression of genes involved with oogenesis and fatty acid metabolism when on its host, many more genes show reduced expression in D. sechellia. For several functionally related genes, this decrease in expression is associated with loss-of-function alleles. The rapid accumulation of these alleles potentially affected D. sechelliaM-bM-^@M-^Ys initial adaptation to M. citrifolia, likely contributes to D. sechelliaM-bM-^@M-^Ys poor competitive ability off of its host, and increases ecological isolation between D. sechellia and its sister species. Keywords: comparative hybridization, gene expression 2 species (simulans and sechella) by choice vs no-choice treatment for octanoic & hexanoic acid blend
2008-11-25 | E-GEOD-13723 | biostudies-arrayexpress