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Abstract In the United Kingdom, roughly 1 in 250 babies are stillborn each year. Most women who experience stillbirth become pregnant again – 80% within a year of loss. Presently, obstetric-led care is recommended; though there is a growing body of evidence to support provision of specialist services. The Rainbow Clinic is a specialist antenatal service providing care for pregnancies after loss incorporating clinical and psychological care. This study aimed to assess patient experience at the Rainbow Clinic and identify areas for clinical improvement. A 13-item questionnaire was distributed to pregnant women who attended the Rainbow Clinics at the Oxford Road and Wythenshawe sites of Saint Mary’s Hospital, Manchester, UK between July 2016 and June 2021. Descriptive statistics and unpaired t-test were used for quantitative data and summative content analysis for qualitative data. Four-hundred and fifty-six women completed the questionnaire. The mean patient experience score per quarter was stable with an average of 21.1 (± 3.0) for the five years, with a maximum attainable score of 25. The COVID-19 pandemic had no effect on patient experience at the Rainbow Clinic (pre-pandemic vs. during-pandemic: mean 21.2 v 21.3; p = 0.75). Free-text responses demonstrated women felt positively about the antenatal care received. Identified areas for improvement included “more awareness of the [Rainbow] sticker” to ensure women with previous loss are identified; increased publicity of the Rainbow Clinic services; developing more clinics at different locations to improve accessibility; and continuing specialist input into intrapartum care. Specialist antenatal care provided by the Rainbow Clinic was rated as of a high standard. Potential future improvements include sticker alterations (or other mechanisms to identify women who have experienced a previous loss) and develop increased awareness of the clinic in other institutions.

Abstract Tephra is a unique volcanic product with an unparalleled role in understanding past eruptions, long-term behavior of volcanoes, and the effects of volcanism on climate and the environment. Tephra deposits also provide spatially widespread, high-resolution time-stratigraphic markers across a range of sedimentary settings and thus are used in numerous disciplines (e.g., volcanology, climate science, archaeology). Nonetheless, the study of tephra deposits is challenged by a lack of standardization that inhibits data integration across geographic regions and disciplines. We present comprehensive recommendations for tephra data gathering and reporting that were developed by the tephra science community to guide future investigators and to ensure that sufficient data are gathered for interoperability. Recommendations include standardized field and laboratory data collection, reporting and correlation guidance. These are organized as tabulated lists of key metadata with their definition and purpose. They are system independent and usable for template, tool, and database development. This standardized framework promotes consistent documentation and archiving, fosters interdisciplinary communication, and improves effectiveness of data sharing among diverse communities of researchers.

Category: Midfoot/Forefoot; Trauma; Other Introduction/Purpose: Metatarsal fractures account for over 35% of all foot fractures, and of these 68% specifically involve the fifth metatarsal [1],[2]. Subgroups of fractures affecting the fifth metatarsal base may be at higher risk of nonunion and therefore benefit from early surgical fixation, but traditional predictive models focus on the location of the fracture and little else. In this study, we aimed to determine predictive factors associated with non-union of fifth metatarsal fractures to assist surgeons and patients, alike, in identifying those at higher risk of nonunion. Methods: A retrospective machine learning-based analysis of 1,000 patients, >=18 y/o, diagnosed with a fifth metatarsal fracture at three tertiary medical centers was conducted. The fifth metatarsal base fracture was confirmed radiographically. We gathered imaging and narrative data including demographics (age, height, weight, BMI, gender, race, smoking habits, activity level), medications, chronic conditions, and fracture status (fracture zone, displacement, treatment method, healing status, and healing time). Non-union was described as failing to heal within 180 days of initial injury [3]. A machine learning analysis together with Pearson's correlation test and T-test were utilized where applicable. Five imputation methods were used to fill in missing datapoints. P