Watching closely: Spatial distance influences theory of mind responding in film viewers

Shot scale in film, that is the apparent spatial distance of characters from the camera, is one of the most important compositional elements in a film shot guiding media audiences’ attention. The primary aim of the present study was to investigate the extent to which apparent spatial distance of fictional characters can evoke theory of mind responding in film viewers. Theory of mind, referring to the capacity of attributing mental states to others, is considered fundamental in audiences’ character involvement and narrative understanding, and it presumably mediates narrative effects. Four short animated movies were annotated for shot scale distribution and presented to participants (N = 52) in a within-subject design. Participants were asked to retell the story of the films and fill in questionnaires on narrative experience. Skin conductance was also measured during exposure. Story-descriptions were content analysed for theory of mind responses. In a Poisson-regression model average spatial distance predicted theory of mind response indicating that increasing spatial proximity triggered higher occurrence of mental state references in participants’ story-descriptions. The findings give insight into how the visual presentation of characters shapes audiences’ mental models of the story

AB1165 MEDICATION ADHERENCE DATA IN A RANDOMIZED TRIAL: LARGE CHALLENGES TO COME FROM RAW DATA TO A WORKABLE AND RELIABLE DATASET

Background:Medication adherence in the GLORIA trial, among elderly patients with rheumatoid arthritis, is measured with caps that register openings of the medication bottle. At each study visit, one or two medication bottles with cap (kits) are dispensed, each containing 90 capsules. Multiple steps are needed to come to a workable dataset to describe adherence.Objectives:To describe the steps that are needed to come from raw data to a workable dataset to analyze adherence data that are recorded by electronic caps.Methods:The medication bottle contains a cap with the ability to register cap openings. The raw dataset from the caps consist of an excel file with one opening event per row, recorded as date and time. One cap yields approximately 90 rows. First, the kit numbers were matched to the corresponding patient numbers, that are recorded in another excel file. Instances where two kits were dispensed were recorded with two kit numbers in one cell and need to be copied to two cells with one kit number. Second, the VLOOKUP function was used to combine dates and kit numbers. One row now contains all openings from one kit. Then, the number of days between first opening and each next opening date was calculated. A range of 90 days was made to calculate how many times the bottle was opened on each day of the 90-days period. The results were color-coded to visualize instances of zero, one or ≥two openings on a day.Results:The colored calendar matrix (Figure 1) can now be used to categorize adherence patterns.Conclusion:A monitoring cap seems a simple instrument to measure adherence. However, multiple steps and a lot of time are needed to come to a workable dataset for the study of adherence patterns.Acknowledgments:The GLORIA project is funded by the European Union's Horizon 2020 research and innovation programme under the topic "Personalizing Health and Care'', grant agreement No 634886.Disclosure of Interests:Linda Hartman: None declared, Elisa Alessandri: None declared, Reinhard Bos: None declared, Daniela Opris-Belinski Speakers bureau: as declared, Marc R Kok Grant/research support from: BMS and Novartis, Consultant of: Novartis and Galapagos, Hanneke Griep-Wentink: None declared, Ruth Klaasen: None declared, Cornelia Allaart: None declared, George Bruyn: None declared, Hennie Raterman Grant/research support from: UCB, Consultant of: Abbvie, Amgen, Bristol-Myers Sqibb, Cellgene and Sanofi Genzyme, Marieke Voshaar Grant/research support from: part of phd research, Speakers bureau: conducting a workshop (Pfizer), Nuno Gomes: None declared, Rui Pinto: None declared, Thomas Klausch: None declared, WIllem Lems Grant/research support from: Pfizer, Consultant of: Lilly, Pfizer, Maarten Boers: None declare

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

Abstract: Purpose: Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis. Methods: A systematic search was performed in PubMed, Embase.com and Scopus. Studies targeting sepsis, severe sepsis or septic shock in any hospital setting were eligible for inclusion. The index test was any supervised machine learning model for real-time prediction of these conditions. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, with a tailored Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist to evaluate risk of bias. Models with a reported area under the curve of the receiver operating characteristic (AUROC) metric were meta-analyzed to identify strongest contributors to model performance. Results: After screening, a total of 28 papers were eligible for synthesis, from which 130 models were extracted. The majority of papers were developed in the intensive care unit (ICU, n = 15; 54%), followed by hospital wards (n = 7; 25%), the emergency department (ED, n = 4; 14%) and all of these settings (n = 2; 7%). For the prediction of sepsis, diagnostic test accuracy assessed by the AUROC ranged from 0.68–0.99 in the ICU, to 0.96–0.98 in-hospital and 0.87 to 0.97 in the ED. Varying sepsis definitions limit pooling of the performance across studies. Only three papers clinically implemented models with mixed results. In the multivariate analysis, temperature, lab values, and model type contributed most to model performance. Conclusion: This systematic review and meta-analysis show that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time. Although they present alternatives to traditional scoring systems, between-study heterogeneity limits the assessment of pooled results. Systematic reporting and clinical implementation studies are needed to bridge the gap between bytes and bedside

Adjusting Measurement Bias in Sequential Mixed-Mode Surveys using re-interview data

In mixed-mode surveys, mode differences in measurement bias, also called measurement effects or mode effects, continue to pose a problem to survey practitioners. In this paper, we discuss statistical adjustment of measurement bias to the level of a measurement benchmark mode in the context of inference from mixed-mode data. Doing so requires auxiliary information, which we suggest collecting in a re-interview administered to a sub-set of respondents to the first stage of a sequential mixed-mode survey. In the re-interview, relevant questions from the main survey are repeated. After introducing the design and presenting relevant statistical theory, this paper evaluates by Monte Carlo simulation the performance of six candidate estimators that exploit re-interview information. In the simulation parameters are systematically varied that define the size and type of measurement and selection effects between modes in the mixedmode design. Our results indicate that the performance of the estimators strongly depends on the true measurement error model. However, one estimator, called the inverse regression estimator, performs particularly well under all considered scenarios. Our results suggest that the re-interview method is a useful approach to adjust measurement effects in the presence of non-ignorable selectivity between modes in mixed-mode data

Disentangling mode-specific selection and measurement bias in social surveys

A large-scale mixed-mode experiment linked to the Dutch Crime Victimization Survey was conducted in 2011. The experiment consisted of two waves; one wave with random assignment to one of the modes web, paper, telephone and face-to-face, and one follow-up wave to the full sample with interviewer modes only. The objective of the experiment is to estimate total mode effects and more specifically the corresponding mode effect components arising from undercoverage, nonresponse and measurement. In this paper, mode-specific selection and measurement bias are defined, and estimators for the bias terms based on the experimental design are introduced and discussed. The proposed estimators are applied to a number of key survey variables from the Labour Force Survey and the Crime Victimization Survey

Wound dehiscences following pre-implant bone augmentation with autogenous iliac crest bone grafts: A retrospective cohort study

Purpose: To evaluate possible risk factors associated with wound dehiscences following pre-implant alveolar bone augmentation with autologous anterior iliac crest bone grafts covered with resorbable collagen membranes or human demineralised bone laminae. Materials and methods: Data of 161 patients who underwent bone augmentation prior to the insertion of dental implants were analysed. The preoperative dental status, locations of alveolar bone augmentation sites and location of wound dehiscences were recorded. Gender, age, smoking, alcohol exposure, and dental and medical histories were reviewed. Information was also collected on the surgeons, augmentation technique, application of a collagen membrane, fixation screw type and suture material. Univariate logistic regression analysis was used to evaluate pre- and perioperative variables as predictors of dehiscences. Results: A total of 42 (26.1%) of the 161 augmented patients developed a wound dehiscence following surgery. Most commonly affected sites were the anterior maxilla, followed by the anterior mandible. Males developed wound dehiscences with higher probability than females (odds ratio female = 0.444; P = 0.025; 95% CI: 0.214 to 0.903). Furthermore, marginal associations (P < 0.10) are found for smoking and an anterior location of the augmentation. Smokers were found to have higher probability of a wound dehiscence (odds ratio 2.089; P = 0.064; 95% CI: 0.957 to 4.500) compared to non-smokers. A posterior location of the augmentation was associated with lower probability of a wound dehiscence (odds ratio 0.188; P = 0.076; 95% CI: 0.035 to 0.802) compared to an anterior location. Conclusions: Based on this study population, smoking in males seems to be the most important risk factor for the development of wound dehiscences after pre-implant alveolar bone augmentation procedures

Wound dehiscences following pre-implant bone augmentation with autogenous iliac crest bone grafts: A retrospective cohort study

Purpose: To evaluate possible risk factors associated with wound dehiscences following pre-implant alveolar bone augmentation with autologous anterior iliac crest bone grafts covered with resorbable collagen membranes or human demineralised bone laminae. Materials and methods: Data of 161 patients who underwent bone augmentation prior to the insertion of dental implants were analysed. The preoperative dental status, locations of alveolar bone augmentation sites and location of wound dehiscences were recorded. Gender, age, smoking, alcohol exposure, and dental and medical histories were reviewed. Information was also collected on the surgeons, augmentation technique, application of a collagen membrane, fixation screw type and suture material. Univariate logistic regression analysis was used to evaluate pre- and perioperative variables as predictors of dehiscences. Results: A total of 42 (26.1%) of the 161 augmented patients developed a wound dehiscence following surgery. Most commonly affected sites were the anterior maxilla, followed by the anterior mandible. Males developed wound dehiscences with higher probability than females (odds ratio female = 0.444; P = 0.025; 95% CI: 0.214 to 0.903). Furthermore, marginal associations (P < 0.10) are found for smoking and an anterior location of the augmentation. Smokers were found to have higher probability of a wound dehiscence (odds ratio 2.089; P = 0.064; 95% CI: 0.957 to 4.500) compared to non-smokers. A posterior location of the augmentation was associated with lower probability of a wound dehiscence (odds ratio 0.188; P = 0.076; 95% CI: 0.035 to 0.802) compared to an anterior location. Conclusions: Based on this study population, smoking in males seems to be the most important risk factor for the development of wound dehiscences after pre-implant alveolar bone augmentation procedures