Family in Rehabilitation, Empowering Carers for Improved Malnutrition Outcomes: Protocol for the FREER Pilot Study

Interventions to improve the nutritional status of older adults and the integration of formal and family care systems are critical research areas to improve the independence and health of aging communities and are particularly relevant in the rehabilitation setting.The primary outcome aimed to determine if the FREER (Family in Rehabilitation: EmpowERing Carers for improved malnutrition outcomes) intervention in malnourished older adults during and postrehabilitation improve nutritional status, physical function, quality of life, service satisfaction, and hospital and aged care admission rates up to 3 months postdischarge, compared with usual care. Secondary outcomes evaluated include family carer burden, carer services satisfaction, and patient and carer experiences. This pilot study will also assess feasibility and intervention fidelity to inform a larger randomized controlled trial.This protocol is for a mixed-methods two-arm historically-controlled prospective pilot study intervention. The historical control group has 30 participants, and the pilot intervention group aims to recruit 30 patient-carer pairs. The FREER intervention delivers nutrition counseling during rehabilitation, 3 months of postdischarge telehealth follow-up, and provides supportive resources using a novel model of patient-centered and carer-centered nutrition care. The primary outcome is nutritional status measured by the Scored Patient-Generated Subjective Global Assessment Score. Qualitative outcomes such as experiences and perceptions of value will be measured using semistructured interviews followed by thematic analysis. The process evaluation addresses intervention fidelity and feasibility.Recruitment commenced on July 4, 2018, and is ongoing with eight patient-carer pairs recruited at the time of manuscript submission.This research will inform a larger randomized controlled trial, with potential for translation to health service policies and new models of dietetic care to support the optimization of nutritional status across a continuum of nutrition care from rehabilitation to home.Australian New Zealand Clinical Trials Registry Number (ACTRN) 12618000338268; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374608&isReview=true (Archived by WebCite at http://www.webcitation.org/74gtZplU2).DERR1-10.2196/12647

Family in Rehabilitation, Empowering Carers for Improved Malnutrition Outcomes: Protocol for the FREER Pilot Study

Interventions to improve the nutritional status of older adults and the integration of formal and family care systems are critical research areas to improve the independence and health of aging communities and are particularly relevant in the rehabilitation setting.The primary outcome aimed to determine if the FREER (Family in Rehabilitation: EmpowERing Carers for improved malnutrition outcomes) intervention in malnourished older adults during and postrehabilitation improve nutritional status, physical function, quality of life, service satisfaction, and hospital and aged care admission rates up to 3 months postdischarge, compared with usual care. Secondary outcomes evaluated include family carer burden, carer services satisfaction, and patient and carer experiences. This pilot study will also assess feasibility and intervention fidelity to inform a larger randomized controlled trial.This protocol is for a mixed-methods two-arm historically-controlled prospective pilot study intervention. The historical control group has 30 participants, and the pilot intervention group aims to recruit 30 patient-carer pairs. The FREER intervention delivers nutrition counseling during rehabilitation, 3 months of postdischarge telehealth follow-up, and provides supportive resources using a novel model of patient-centered and carer-centered nutrition care. The primary outcome is nutritional status measured by the Scored Patient-Generated Subjective Global Assessment Score. Qualitative outcomes such as experiences and perceptions of value will be measured using semistructured interviews followed by thematic analysis. The process evaluation addresses intervention fidelity and feasibility.Recruitment commenced on July 4, 2018, and is ongoing with eight patient-carer pairs recruited at the time of manuscript submission.This research will inform a larger randomized controlled trial, with potential for translation to health service policies and new models of dietetic care to support the optimization of nutritional status across a continuum of nutrition care from rehabilitation to home.Australian New Zealand Clinical Trials Registry Number (ACTRN) 12618000338268; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374608&isReview=true (Archived by WebCite at http://www.webcitation.org/74gtZplU2).DERR1-10.2196/12647

Widening Access to Applied Machine Learning with TinyML

Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this paper, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML both leverages low-cost and globally accessible hardware, and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia (Harvard University) and industry (Google) produced a four-part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for learners from a global variety of backgrounds. It introduces pupils to real-world applications, ML algorithms, data-set engineering, and the ethical considerations of these technologies via hands-on programming and deployment of TinyML applications in both the cloud and their own microcontrollers. To facilitate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project competition. We also released the course materials publicly, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies.Comment: Understanding the underpinnings of the TinyML edX course series: https://www.edx.org/professional-certificate/harvardx-tiny-machine-learnin

Postmenopausal Hormone Therapy and Colorectal Cancer Risk by Molecularly Defined Subtypes and Tumor Location

Background: Postmenopausal hormone therapy (HT) is associated with a decreased colorectal cancer (CRC) risk. As CRC is a heterogeneous disease, we evaluated whether the association of HT and CRC differs across etiologically relevant, molecularly defined tumor subtypes and tumor location. Methods: We pooled data on tumor subtypes (microsatellite instability status, CpG island methylator phenotype status, BRAF and KRAS mutations, pathway: adenoma-carcinoma, alternate, serrated), tumor location (proximal colon, distal colon, rectum), and HT use among 8220 postmenopausal women (3898 CRC cases and 4322 controls) from 8 observational studies. We used multinomial logistic regression to estimate odds ratios (OR) and 95% confidence intervals (CIs) for the association of ever vs never HT use with each tumor subtype compared with controls. Models were adjusted for study, age, body mass index, smoking status, and CRC family history. All statistical tests were 2-sided. Results: Among postmenopausal women, ever HT use was associated with a 38% reduction in overall CRC risk (OR =0.62, 95% CI = 0.56 to 0.69). This association was similar according to microsatellite instability, CpG island methylator phenotype and BRAF or KRAS status. However, the association was attenuated for tumors arising through the serrated pathway (OR = 0.81, 95% CI = 0.66 to 1.01) compared with the adenoma-carcinoma pathway (OR = 0.63, 95% CI = 0.55 to 0.73; P het =.04) and alternate pathway (OR = 0.61, 95% CI = 0.51 to 0.72). Additionally, proximal colon tumors had a weaker association (OR = 0.71, 95% CI = 0.62 to 0.80) compared with rectal (OR = 0.54, 95% CI = 0.46 to 0.63) and distal colon (OR = 0.57, 95% CI = 0.49 to 0.66; P het =.01) tumors. Conclusions: We observed a strong inverse association between HT use and overall CRC risk, which may predominantly reflect a benefit of HT use for tumors arising through the adenoma-carcinoma and alternate pathways as well as distal colon and rectal tumors

Association Between Smoking and Molecular Subtypes of Colorectal Cancer

Background: Smoking is associated with colorectal cancer (CRC) risk. Previous studies suggested this association may be restricted to certain molecular subtypes of CRC, but large-scale comprehensive analysis is lacking. Methods: A total of 9789 CRC cases and 11 231 controls of European ancestry from 11 observational studies were included. We harmonized smoking variables across studies and derived sex study-specific quartiles of pack-years of smoking for analysis. Four somatic colorectal tumor markers were assessed individually and in combination, including BRAF mutation, KRAS mutation, CpG island methylator phenotype (CIMP), and microsatellite instability (MSI) status. A multinomial logistic regression analysis was used to assess the association between smoking and risk of CRC subtypes by molecular characteristics, adjusting for age, sex, and study. All statistical tests were 2-sided and adjusted for Bonferroni correction. Results: Heavier smoking was associated with higher risk of CRC overall and stratified by individual markers (P-trend <.001). The associations differed statistically significantly between all molecular subtypes, which was the most statistically significant for CIMP and BRAF. Compared with never-smokers, smokers in the fourth quartile of pack-years had a 90% higher risk of CIMP-positive CRC (odds ratio = 1.90, 95% confidence interval = 1.60 to 2.26) but only 35% higher risk for CIMP-negative CRC (odds ratio = 1.35, 95% confidence interval = 1.22 to 1.49; P-difference = 2.1 x 10(-6)). The association was also stronger in tumors that were CIMP positive, MSI high, or KRAS wild type when combined (P-difference <.001). Conclusion: Smoking was associated with differential risk of CRC subtypes defined by molecular characteristics. Heavier smokers had particularly higher risk of CRC subtypes that were CIMP positive and MSI high in combination, suggesting that smoking may be involved in the development of colorectal tumors via the serrated pathway

DataPerf: Benchmarks for Data-Centric AI Development

Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithfulness of the underlying problems. Neglecting the fundamental importance of data has given rise to inaccuracy, bias, and fragility in real-world applications, and research is hindered by saturation across existing dataset benchmarks. In response, we present DataPerf, a community-led benchmark suite for evaluating ML datasets and data-centric algorithms. We aim to foster innovation in data-centric AI through competition, comparability, and reproducibility. We enable the ML community to iterate on datasets, instead of just architectures, and we provide an open, online platform with multiple rounds of challenges to support this iterative development. The first iteration of DataPerf contains five benchmarks covering a wide spectrum of data-centric techniques, tasks, and modalities in vision, speech, acquisition, debugging, and diffusion prompting, and we support hosting new contributed benchmarks from the community. The benchmarks, online evaluation platform, and baseline implementations are open source, and the MLCommons Association will maintain DataPerf to ensure long-term benefits to academia and industry.Comment: NeurIPS 2023 Datasets and Benchmarks Trac

Preoperative Aspirin Use and Its Effect on Adverse Events in Patients Undergoing Cardiac Operations

BackgroundPreoperative aspirin use within 5 days of cardiac operations is controversial. Aspirin could reduce cardiovascular complications and yet might increase risk of bleeding. Recent reports showed conflicting results, and whether aspirin has variable effects for different cardiac surgical procedures is unclear.MethodsA single-center retrospective cohort analysis was performed. After propensity score matching (PSM) for identified confounders, the relationship between preoperative aspirin use and 30-day all-cause mortality, postoperative renal failure, major adverse cardiocerebral events (MACE), blood transfusion, reoperation for bleeding, and postoperative infection were estimated with separate logistic regression models.ResultsPreoperative aspirin therapy was associated with a 49% (p = 0.04) increased risk of reoperation for bleeding among 868 matched pairs of patients undergoing valve operations. Among 725 matched patients undergoing coronary artery bypass grafting (CABG), preoperative aspirin therapy was not associated with a statistically significant higher risk of reoperation for bleeding. However, preoperative aspirin use, compared with nonuse, was not associated with risks of MACE, 30-day mortality, postoperative renal failure, blood transfusion, or postoperative infection in the entire cohort, in patients undergoing valve operations only, and in patients undergoing CABG only after PSM.ConclusionsPreoperative aspirin use in all patients undergoing cardiac operations was not associated with risks of major cardiac, cerebral, or renal complications and infections and death; however, the risk of reoperation for bleeding was elevated among preoperative aspirin users compared with nonusers in a subpopulation of patients undergoing valve operations only

Widening Access to Applied Machine Learning With TinyML

Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML leverages low-cost and globally accessible hardware and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia and industry produced a four part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for global learners from a variety of backgrounds. It introduces real-world applications, ML algorithms, data-set engineering, and the ethi- cal considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on their own microcontrollers. To facili- tate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project com- petition. We also open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies