Experimental model of resection craniotomy with subsequent reconstruction of the defect with a polymer implant enables comprehensive assessment of functional and ultrastructural changes during replacement of the damaged tissue. Reconstruction of a skull defect was accompanied by transient motor disturbance in the acute period and did not cause functional disorders and neurological deficits in a delayed period. Histological examination of osteal and brain tissue revealed no pathological reactions that could be associated with the response to the chemical components of the implant.

UNASSIGNED: Within Australia, some families face challenges in accessing paediatric speech-language pathology services. This research sought to investigate the factors that impact access to paediatric speech-language pathology services within Western Australia.
UNASSIGNED: Researchers used constructivist grounded theory to investigate the construct of access, as experienced and perceived by service decision-makers, namely caregivers of children with communication needs and speech-language pathologists who provide communication services. Eleven speech-language pathologists and 16 caregivers took part in 32 semi-structured in-depth interviews. Researchers used layers of coding of interviews transcripts and the constant comparative method to investigate data.
UNASSIGNED: Findings outline the factors that impact access to speech-language pathology services, as organised into the seven categories of the Model of Access to Speech-Language Pathology Services (MASPS). The categories and properties of this model are grounded within experiences and perspectives that participants contributed to the dataset.
UNASSIGNED: MASPS provides a theoretical structure that has been constructed using inductive and abductive reasoning. This model can be used by service designers and decision-makers to reflect upon and improve experiences of service for a range of consumers. MASPS can also be used as a basis for further investigation into aspects of service access.

[This corrects the article DOI: 10.3389/fpubh.2023.1136939.].

The rising global incidence of cancer makes it the second leading cause of death worldwide. Over the past decades, significant progress has been made in both basic knowledge and the discovery of new therapeutic approaches. However, the complexity of mechanisms related to tumor development requires the use of sophisticated and adapted research tools. Among these, the fruitfly Drosophila melanogaster represents a powerful genetic model with numerous practical and conceptual advantages. Indeed, the conservation of genes implicated in cancer between this insect and mammals places Drosophila as a crucial genetic tool for understanding the fundamental mechanisms governing tumorigenesis and identifying new therapeutic targets. This review aims to describe this original model and demonstrate its relevance for studying cancer biology.

BACKGROUND: Named entity recognition (NER) is a fundamental task in natural language processing. However, it is typically preceded by named entity annotation, which poses several challenges, especially in the clinical domain. For instance, determining entity boundaries is one of the most common sources of disagreements between annotators due to questions such as whether modifiers or peripheral words should be annotated. If unresolved, these can induce inconsistency in the produced corpora, yet, on the other hand, strict guidelines or adjudication sessions can further prolong an already slow and convoluted process.
OBJECTIVE: The aim of this study is to address these challenges by evaluating 2 novel annotation methodologies, lenient span and point annotation, aiming to mitigate the difficulty of precisely determining entity boundaries.
METHODS: We evaluate their effects through an annotation case study on a Japanese medical case report data set. We compare annotation time, annotator agreement, and the quality of the produced labeling and assess the impact on the performance of an NER system trained on the annotated corpus.
RESULTS: We saw significant improvements in the labeling process efficiency, with up to a 25% reduction in overall annotation time and even a 10% improvement in annotator agreement compared to the traditional boundary-strict approach. However, even the best-achieved NER model presented some drop in performance compared to the traditional annotation methodology.
CONCLUSIONS: Our findings demonstrate a balance between annotation speed and model performance. Although disregarding boundary information affects model performance to some extent, this is counterbalanced by significant reductions in the annotator\'s workload and notable improvements in the speed of the annotation process. These benefits may prove valuable in various applications, offering an attractive compromise for developers and researchers.

High shear wet granulation (HSWG) is widely used in tablet manufacturing mainly because of its advantages in improving flowability, powder handling, process run time, size distribution, and preventing segregation. In line process analytical technology measurements are essential in capturing detailed particle dynamics and presenting real-time data to uncover the complexity of the HSWG process and ultimately for process control. This study presents an opportunity to predict the properties of the granules and tablets through torque measurement of the granulation bowl and the force exerted on a novel force probe within the powder bed. Inline force measurements are found to be more sensitive than torque measurements to the granulation process. The characteristic force profiles present the overall fingerprint of the high shear wet granulation, in which the evolution of the granule formation can improve our understanding of the granulation process. This provides rich information relating to the properties of the granules, identification of the even distribution of the binder liquid, and potential granulation end point. Data were obtained from an experimental high shear mixer across a range of key process parameters using a face-centred surface response design of experiment (DoE). A closed-form analytical model was developed from the DOE matrix using the discovery of evolutionary equations. The model is able to provide a strong predictive indication of the expected tablet tensile strength based only on the data in-line. The use of a closed form mathematical equation carries notable advantages over other AI methodologies such as artificial neural networks, notably improved interpretability/interrogability, and minimal inference costs, thus allowing the model to be used for real-time decision making and process control. The capability of accurately predicting, in real time, the required compaction force required to achieve the desired tablet tensile strength from upstream data carries the potential to ensure compression machine settings rapidly reach and are maintained at optimal values, thus maximising efficiency and minimising waste.

EUS interventions have an increasing role in the treatment for hepatobiliary-pancreatic diseases. However, the procedure itself is not frequently performed, needs expertise, and carries a high risk of complications. With these limitations, the hands-on practice model is very important for the endoscopist in training for EUS intervention. There have been various hands-on models for EUS interventions, ranging from in vivo living pig model to all-synthetic model. Although a living model provides realistic sensation, the preparation is complex and increases concerns for zoonotic issues. All-synthetic models are easier to prepare and store but not realistic and still need the room for improvement. Hybrid ex vivo model is more widely available and provides various training procedures but still needs special preparation for the porcine tissue.

UNASSIGNED: The purpose of this review is to summarize the characteristics and applications of current hysteroscopic training models.
UNASSIGNED: We conducted a systematic search of PubMed, Embase, and Cochrane Library for eligible studies published before March 2024. Manual screening of references and citation tracking were also performed.
UNASSIGNED: Reported hysteroscopic training models included virtual reality simulators, non-biological material models, plant tissue models, animal tissue models, and human tissue models. No training model was distinctly superior in terms of realism, haptic feedback, availability of standardized scoring of operations, preparation difficulty, reusability of surgical procedure, and prices. Utilizing any type of models for hysteroscopy simulation training could assist trainees in enhancing relevant knowledge, skills, self-confidence, and comfort, but virtual reality models had an advantage in training capacity.
UNASSIGNED: Each hysteroscopic training model has its advantages and disadvantages. An appropriate training curriculum is needed to efficiently leverage the merits of different models. The realism and training effectiveness of various training models need to be compared using rigorously designed studies and standard evaluation tools.

BACKGROUND: Theories, models and frameworks (TMFs) are useful when implementing, evaluating and sustaining healthcare evidence-based interventions. Yet it can be challenging to identify an appropriate TMF for an implementation project. We developed and tested the usability of an online tool to help individuals who are doing or supporting implementation practice activities to identify appropriate models and/or frameworks to inform their work.
METHODS: We used methods guided by models and evidence on implementation science and user-centered design. Phases of tool development included applying findings from a scoping review of TMFs and interviews with 24 researchers/implementers on barriers and facilitators to identifying and selecting TMFs. Based on interview findings, we categorized the TMFs by aim, stage of implementation, and target level of change to inform the tool\'s algorithm. We then conducted interviews with 10 end-users to test the usability of the prototype tool and administered the System Usability Scale (SUS). Usability issues were addressed and incorporated into the tool.
RESULTS: We developed Find TMF, an online tool consisting of 3-4 questions about the user\'s implementation project. The tool\'s algorithm matches key characteristics of the user\'s project (aim, stage, target change level) with characteristics of different TMFs and presents a list of candidate models/frameworks. Ten individuals from Canada or Australia participated in usability testing (mean SUS score 84.5, standard deviation 11.4). Overall, participants found the tool to be simple, easy to use and visually appealing with a useful output of candidate models/frameworks to consider for an implementation project. Users wanted additional instruction and guidance on what to expect from the tool and how to use the information in the output table. Tool improvements included incorporating an overview figure outlining the tool steps and output, displaying the tool questions on a single page, and clarifying the available functions of the results page, including adding direct links to the glossary and to complementary tools.
CONCLUSIONS: Find TMF is an easy-to-use online tool that may benefit individuals who support implementation practice activities by making the vast number of models and frameworks more accessible, while also supporting a consistent approach to identifying and selecting relevant TMFs.

BACKGROUND: Emergency department crowding continues to threaten patient safety and cause poor patient outcomes. Prior models designed to predict hospital admission have had biases. Predictive models that successfully estimate the probability of patient hospital admission would be useful in reducing or preventing emergency department \"boarding\" and hospital \"exit block\" and would reduce emergency department crowding by initiating earlier hospital admission and avoiding protracted bed procurement processes.
OBJECTIVE: To develop a model to predict imminent adult patient hospital admission from the emergency department early in the patient visit by utilizing existing clinical descriptors (ie, patient biomarkers) that are routinely collected at triage and captured in the hospital\'s electronic medical records. Biomarkers are advantageous for modeling due to their early and routine collection at triage; instantaneous availability; standardized definition, measurement, and interpretation; and their freedom from the confines of patient histories (ie, they are not affected by inaccurate patient reports on medical history, unavailable reports, or delayed report retrieval).
METHODS: This retrospective cohort study evaluated 1 year of consecutive data events among adult patients admitted to the emergency department and developed an algorithm that predicted which patients would require imminent hospital admission. Eight predictor variables were evaluated for their roles in the outcome of the patient emergency department visit. Logistic regression was used to model the study data.
RESULTS: The 8-predictor model included the following biomarkers: age, systolic blood pressure, diastolic blood pressure, heart rate, respiration rate, temperature, gender, and acuity level. The model used these biomarkers to identify emergency department patients who required hospital admission. Our model performed well, with good agreement between observed and predicted admissions, indicating a well-fitting and well-calibrated model that showed good ability to discriminate between patients who would and would not be admitted.
CONCLUSIONS: This prediction model based on primary data identified emergency department patients with an increased risk of hospital admission. This actionable information can be used to improve patient care and hospital operations, especially by reducing emergency department crowding by looking ahead to predict which patients are likely to be admitted following triage, thereby providing needed information to initiate the complex admission and bed assignment processes much earlier in the care continuum.