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Proton pump inhibitors (PPIs) are among the most commonly and inappropriately prescribed drugs by general practitioners (GPs), resulting in increased risk of adverse outcomes for patients and in avoidable costs for Italy's National Health Service (NHS). This study aims to assess the effectiveness of a low-cost and easily implementable informative intervention directed at GPs to enhance the appropriate prescription of PPIs. The LAPTOP-PPI study is a pragmatic, cluster-randomized controlled trial designed to improve the appropriateness of PPI prescriptions among community-dwelling individuals aged ≥65 years. In June 2021, GPs in the Local Health Units (LHUs) of Bergamo (Northern Italy) and Caserta (Southern Italy) were randomly allocated to either an intervention group (summary reports on prescribing habits, scientific documents on the Italian Medicine Agency's therapeutic indications, strategies for PPI de-prescribing, along with educational materials for patients), and a control group (standard practice). PPI appropriateness was assessed through an algorithm specifically designed and based on NHS prescription appropriateness and reimbursement criteria. Intervention efficacy was evaluated by comparing data from the baseline period (July 1 to 31 December 2019) with those from the follow-up period (July 1 to 31 December 2021), 6 months after randomization. The analysis was performed on the intention-to-treat principle and according to GP level. To estimate the effectiveness of the intervention, we used a difference-in-differences (DID) approach. Overall, 942 GPs (540 from Bergamo and 402 from Caserta LHUs) were included in the analysis. At baseline, 171,978 patients aged ≥65 received drug prescriptions for acid-related diseases and were assessable for evaluation of appropriateness. At follow-up, this number was 137,699. The overall inappropriateness rate at baseline among GPs included in the analysis was 57.4% (std.dev. 8.4%) in the intervention arm and 57.6% (std.dev. 8.8%) in the control arm; 6 months after the intervention delivery, they were 59.2% (std.dev. 8.0%) and 58.5% (std.dev. 7.3%), respectively. Given their widespread use, improving the prescription quality of PPIs is a major concern. Educational interventions for GPs and patients are routine strategies to address inappropriateness, but they appear to be insufficient for independently improving prescribing practice, especially in a critical situation such as the post-pandemic period.

Purpose To assess the effectiveness of an informative intervention on general practitioners aimed at improving patients’ adherence to statin therapy. Methods In the local health unit (LHU) of Bergamo, Lombardy (Italy), each general practitioner received a synthetic scientific document on dyslipidaemia and statins and aggregated data on adherence in 2006 for his/her patients compared to the means in the LHU and in his/her working district. Furthermore, a sample of seven districts received also a table of adherence levels for single patients. Patient’s level data were retrieved from the health care utilisation databases of the LHU. Adherence parameters (proportion of patients with only one prescription, medication possession ratio [MPR] and proportion of non-persistent patients) were assessed after 1 year of follow-up. Results Overall, 5833 and 4788 new statin users were enrolled before and after the intervention, respectively. The percentage of patients with only one prescription decreased from 28.0 to 23.9 % ( p  < 0.001). MPR increased from 70.3 to 76.0 % ( p  < 0.001), and proportion of patients with MPR ≥80 % increased from 45.4 to 56.4 % ( p  < 0.001). The persistence also showed an improvement, both in terms of decreasing proportion of non-persistent (from 51.9 to 41.4 %, p  < 0.001) and of increasing duration of continued therapy (from 235 to 264 mean days of persistent therapy, p  < 0.001). There were not significant differences between the two types of intervention. Conclusions This intervention resulted in an overall improvement of the short-term adherence to therapy. This tool can be replicated in other local contexts and with other chronic therapies.

Theories occupy different positions in the scientific circle of enquiry as they vary in scope, abstraction, and complexity. Mid-range theories play a crucial bridging role between raw empirical observations and all-encompassing grand-theoretical schemes. A shift of perspective from ‘theories’ as products to ‘theorising’ as a process can enable empirical researchers to capitalise on the two-way relationships between empirical data and different levels of theory and contribute to the advancement of knowledge. This can be facilitated by embracing theoretically informative (in addition to merely theoretically informed) research, developing mechanism-based explanations, and broadening the repertoire of grand-theoretical orientations.

Purpose Dental anxiety (DA) is characterized by the expression of tension, stress, apprehension, irritation, anger, and frustration experienced by patients during dental appointment. The objective of this study was to systematically review the literature to assess the effectiveness of the use of informative videos in reducing DA in patients undergoing 3 M surgeries. Methods Searches were carried out on MEDLINE (via PubMed), the Cochrane Central Registry of Controlled Trials (CENTRAL), the Virtual Health Library (VHL), and the Web of Science. Articles published until November 20, 2021, were included. There were no restrictions on the data or language of publication. Results A total of 9 randomized clinical trials were included in this review, and five studies were included in the meta-analysis, comprising 529 patients. There was no significant difference in DA between the groups in the baseline when it was evaluated by any of the tools, indicating sample balancing at the beginning of the study. After intervention (video vs. verbal and/or written orientation) in the preoperative period, DA was assessed again; however, there was no difference in DA between the groups when assessed by the MDAS or STAI-S tools. After 3 M removals, the DA was still not significantly different between the groups when measured by the different considered tools. Conclusion Informative videos addressing 3 M removal surgeries used in the preoperative period did not show an influence on the reduction of pre- and postoperative DA when compared to the verbal and/or written informative presentation.

Prevention of binge eating through just-in-time mobile interventions requires the prediction of respective high-risk times, for example, through preceding affective states or associated contexts. However, these factors and states are highly idiographic; thus, prediction models based on averages across individuals often fail. We developed an idiographic, within-individual binge-eating prediction approach based on ecological momentary assessment (EMA) data. We first derived a novel EMA-item set that covers a broad set of potential idiographic binge-eating antecedents from literature and an eating disorder focus group (n=11). The final EMA-item set (6 prompts per day for 14 days) was assessed in female patients with bulimia nervosa or binge-eating disorder. We used a correlation-based machine learning approach (Best Items Scale that is Cross-validated, Unit-weighted, Informative, and Transparent) to select parsimonious, idiographic item subsets and predict binge-eating occurrence from EMA data (32 items assessing antecedent contextual and affective states and 12 time-derived predictors). On average 67.3 (SD 13.4; range 43-84) EMA observations were analyzed within participants (n=13). The derived item subsets predicted binge-eating episodes with high accuracy on average (mean area under the curve 0.80, SD 0.15; mean 95% CI 0.63-0.95; mean specificity 0.87, SD 0.08; mean sensitivity 0.79, SD 0.19; mean maximum reliability of r 0.40, SD 0.13; and mean r 0.13, SD 0.31). Across patients, highly heterogeneous predictor sets of varying sizes (mean 7.31, SD 1.49; range 5-9 predictors) were chosen for the respective best prediction models. Predicting binge-eating episodes from psychological and contextual states seems feasible and accurate, but the predictor sets are highly idiographic. This has practical implications for mobile health and just-in-time adaptive interventions. Furthermore, current theories around binge eating need to account for this high between-person variability and broaden the scope of potential antecedent factors. Ultimately, a radical shift from purely nomothetic models to idiographic prediction models and theories is required.

Applying a robot system in ultrasound-guided percutaneous intervention is an effective approach for prostate cancer diagnosis and treatment. The limited space for robot manipulation restricts structure volume and motion. In this paper, an 8-degree-of-freedom robot system is proposed for ultrasound probe manipulation, needle positioning, and needle insertion. A novel parallel structure is employed in the robot system for space saving, structural rigidity, and collision avoidance. The particle swarm optimization method based on informative value is proposed for kinematic parameter identification to calibrate the parallel structure accurately. The method identifies parameters in the modified kinematic model stepwise according to parameter discernibility. Verification experiments prove that the robot system can realize motions needed in targeting. By applying the calibration method, a reasonable, reliable forward kinematic model is built, and the average errors can be limited to 0.963 and 1.846 mm for insertion point and target point, respectively.

Evaluating engagement with an intervention is a key component of understanding its efficacy. With an increasing interest in developing behavioral interventions in the mobile health (mHealth) space, appropriate methods for evaluating engagement in this context are necessary. Data collected to evaluate mHealth interventions are often collected much more frequently than those for clinic-based interventions. Additionally, missing data on engagement is closely linked to level of engagement resulting in the potential for informative missingness. Thus, models that can accommodate intensively collected data and can account for informative missingness are required for unbiased inference when analyzing engagement with an mHealth intervention. The objectives of this paper are to discuss the utility of the joint modeling approach in the analysis of longitudinal engagement data in mHealth research and to illustrate the application of this approach using data from an mHealth intervention designed to support illness management among people with schizophrenia. Engagement data from an evaluation of an mHealth intervention designed to support illness management among people with schizophrenia is analyzed. A joint model is applied to the longitudinal engagement outcome and time-to-dropout to allow unbiased inference on the engagement outcome. Results are compared to a naïve model that does not account for the relationship between dropout and engagement. The joint model shows a strong relationship between engagement and reduced risk of dropout. Using the mHealth app 1 day more per week was associated with a 23% decreased risk of dropout (P<.001). The decline in engagement over time was steeper when the joint model was used in comparison with the naïve model. Naïve longitudinal models that do not account for informative missingness in mHealth data may produce biased results. Joint models provide a way to model intensively collected engagement outcomes while simultaneously accounting for the relationship between engagement and missing data in mHealth intervention research.

Esophagogastroduodenoscopy (EGD) has been widely applied for gastrointestinal (GI) examinations. However, there is a lack of mature technology to evaluate the quality of the EGD inspection process. In this Letter, the authors design a multi-task anatomy detection convolutional neural network (MT-AD-CNN) to evaluate the EGD inspection quality by combining the detection task of the upper digestive tract with ten anatomical structures and the classification task of informative video frames. The authors’ model is able to eliminate non-informative frames of the gastroscopic videos and detect the anatomies in real time. Specifically, a sub-branch is added to the detection network to classify NBI images, informative and non-informative images. By doing so, the detected box will be only displayed on the informative frames, which can reduce the false-positive rate. They can determine the video frames on which each anatomical location is effectively examined, so that they can analyse the diagnosis quality. Their method reaches the performance of 93.74% mean average precision for the detection task and 98.77% accuracy for the classification task. Their model can reflect the detailed circumstance of the gastroscopy examination process, which shows application potential in improving the quality of examinations.

This paper is a study of Japanese and Sinhala wh-questions, both of which employ a special particle called a Q-particle, ka in Japanese and də in Sinhala, forming QP. A Q-particle is normally base-generated adjacent to a wh-phrase or at the edge of an island when a wh-phrase is inside. However, under very restricted circumstances, a Q-particle can merge with TP, and the whole TP can be pied-piped to spec of CP. An information-seeking wh-question normally represents a set of unvalued propositions (i.e. Hamblin set); however, we claim there are cases in which a set of true propositions (i.e. Karttunen set) can be the meaning of an information-seeking wh-question, and this happens when TP pied-piping is applied. Each circumstance in which such pied-piping is possible is carefully analyzed.

Background When designing and analysing clinical trials, using previous relevant information, perhaps in the form of evidence syntheses, can reduce research waste. We conducted the INVEST (INVestigating the use of Evidence Synthesis in the design and analysis of clinical Trials) survey to summarise the current use of evidence synthesis in trial design and analysis, to capture opinions of trialists and methodologists on such use, and to understand any barriers. Methods Our sampling frame was all delegates attending the International Clinical Trials Methodology Conference in November 2015. Respondents were asked to indicate (1) their views on the use of evidence synthesis in trial design and analysis, (2) their own use during the past 10 years and (3) the three greatest barriers to use in practice. Results Of approximately 638 attendees of the conference, 106 (17%) completed the survey, half of whom were statisticians. Support was generally high for using a description of previous evidence, a systematic review or a meta-analysis in trial design. Generally, respondents did not seem to be using evidence syntheses as often as they felt they should. For example, only 50% (42/84 relevant respondents) had used a meta-analysis to inform whether a trial is needed compared with 74% (62/84) indicating that this is desirable. Only 6% (5/81 relevant respondents) had used a value of information analysis to inform sample size calculations versus 22% (18/81) indicating support for this. Surprisingly large numbers of participants indicated support for, and previous use of, evidence syntheses in trial analysis. For example, 79% (79/100) of respondents indicated that external information about the treatment effect should be used to inform aspects of the analysis. The greatest perceived barrier to using evidence synthesis methods in trial design or analysis was time constraints, followed by a belief that the new trial was the first in the area. Conclusions Evidence syntheses can be resource-intensive, but their use in informing the design, conduct and analysis of clinical trials is widely considered desirable. We advocate additional research, training and investment in resources dedicated to ways in which evidence syntheses can be undertaken more efficiently, offering the potential for cost savings in the long term.