Postoperative pulmonary complications (PPCs) occur frequently and are associated with substantial morbidity and mortality. Evidence suggests that reduction of PPCs can be accomplished by using lung-protective ventilation strategies intraoperatively, but a consensus on perioperative management has not been established. We sought to determine recommendations for lung protection for the surgical patient at an international consensus development conference. Seven experts produced 24 questions concerning preoperative assessment and intraoperative mechanical ventilation for patients at risk of developing PPCs. Six researchers assessed the literature using questions as a framework for their review. The modified Delphi method was utilised by a team of experts to produce recommendations and statements from study questions. An expert consensus was reached for 22 recommendations and four statements. The following are the highlights: (i) a dedicated score should be used for preoperative pulmonary risk evaluation; and (ii) an individualised mechanical ventilation may improve the mechanics of breathing and respiratory function, and prevent PPCs. The ventilator should initially be set to a tidal volume of 6-8 ml kg-1 predicted body weight and positive end-expiratory pressure (PEEP) 5 cm H2O. PEEP should be individualised thereafter. When recruitment manoeuvres are performed, the lowest effective pressure and shortest effective time or fewest number of breaths should be used.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Microlearning, the acquisition of knowledge or skills in the form of small units, is endorsed by health professions educators as a means of facilitating student learning, training, and continuing education, but it is difficult to define in terms of its features and outcomes. Objective: The goal of this review was to conduct a systematic search of the literature on microlearning in health professions education to identify key concepts, characterize microlearning as an educational strategy, and evaluate pedagogical outcomes experienced by health professions students. Methods: A scoping review was performed in 2018 using the bibliographic databases PubMed (MEDLINE), CINAHL, Education Resources Information Center, Embase, PsycINFO, Education Full Text (H.W. Wilson), and ProQuest Dissertations & Theses Global. A combination of keywords and subject headings related to microlearning, e-learning, or just-in-time learning combined with health professions education were used. No date limits were placed on the search, but inclusion was limited to materials published in English. Pedagogical outcomes were evaluated on the basis of the four-level Kirkpatrick’s model. Results: A total of 3,096 references were retrieved, of which 17 articles were selected after applying the inclusion and exclusion criteria. Articles that met the criteria were published between 2011 and 2018, and their authors were from a range of countries including the United States, China, India, Australia, Canada, Iran, Netherlands, Taiwan, and the United Kingdom. The 17 studies reviewed included various health-related disciplines, such as medicine, nursing, pharmacy, dentistry, and allied health. While microlearning appeared in a variety of subject areas, different technologies, such as podcast, short messaging service, microblogging, and social networking service, were also used. Based on Buchem and Hamelmann’s 10 microlearning concepts, each study satisfied at least 40% of the characteristics while all studies featured concepts of maximum time spent less than 15 minutes as well as content aggregation. According to our assessment of each article using the Kirkpatrick's model, 16 (94%) assessed student reactions to the microlearning (level 1); 14 (82%) evaluated knowledge or skill acquisition (level 2); 5 (29%) measured the effect of the microlearning on student behavior (level 3), and no studies were found at the highest level. Conclusions: Microlearning as an educational strategy has demonstrated a positive effect on the knowledge and confidence of health professions students in performing procedures, retaining medication knowledge, studying, and engaging in collaborative learning. However, downsides to microlearning include pedagogical discomfort, technology inequalities, and privacy concerns. Future research should look at higher-level outcomes including benefits to patients or practice changes. The findings of this scoping review will inform education researchers, faculty, and academic administrators on the application of microlearning, pinpoint gaps in the literature, and help identify opportunities for instructional designers and subject matter experts to improve course content in didactic and clinical settings.

Background: Artificial intelligence (AI) is reshaping neuroimaging workflows for Alzheimer’s disease (AD) diagnosis, particularly through PET and MRI analysis advances. Since the FDA approval of Tauvid, a PET tracer targeting tau pathology, there has been a notable increase in studies applying AI to neuroimaging data. This narrative review synthesizes recent, high-impact literature to highlight clinically relevant AI applications in AD imaging. Methods: This review examined peer-reviewed studies published between January 2020 and January 2025, focusing on the use of AI, including machine learning, deep learning, and hybrid models for diagnostic and prognostic tasks in AD using PET and/or MRI. Studies were identified through targeted PubMed, Scopus, and Embase searches, emphasizing methodological diversity and clinical relevance. Results: A total of 109 studies were categorized into five thematic areas: Image preprocessing and segmentation, diagnostic classification, prognosis and disease staging, multimodal data fusion, and emerging innovations. Deep learning models such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer-based architectures were widely employed by the research community in the field of AD. At the same time, several models reported strong diagnostic performance, but methodological challenges such as reproducibility, small sample sizes, and lack of external validation limit clinical translation. Trends in explainable AI, synthetic imaging, and integration of clinical biomarkers are also discussed. Conclusions: AI is rapidly advancing the field of AD imaging, offering tools for enhanced segmentation, staging, and early diagnosis. Multimodal approaches and biomarker-guided models show particular promise. However, future research must focus on reproducibility, interpretability, and standardized validation to bridge the gap between research and clinical practice.

Review questions: How is microlearning defined and designed as an educational strategy in health professions education?What outcomes associated with microlearning have been measured in health professions students?