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Background: Our prior six-year review (n=2165) revealed 24% of patients undergoing posterior decompression surgeries (laminectomy or discectomy) sought emergency department (ED) care within three months post-surgery. We established an integrated Spine Assessment Clinic (SAC) to enhance patient outcomes and minimize unnecessary ED visits through pre-operative education, targeted QI interventions, and early post-operative follow-up. Methods: We reviewed 13 months of posterior decompression data (n=205) following SAC implementation. These patients received individualized, comprehensive pre-operative education and follow-up phone calls within 7 days post-surgery. ED visits within 90 days post-surgery were tracked using provincial databases and compared to our pre-SAC implementation data. Results: Out of 205 patients, 24 (11.6%) accounted for 34 ED visits within 90 days post-op, showing a significant reduction in ED visits from 24% to 11.6%, and decreased overall ED utilization from 42.1% to 16.6% (when accounting for multiple visits by the same patient). Early interventions including wound monitoring, outpatient bloodwork, and prescription adjustments for pain management, helped mitigate ED visits. Patient satisfaction surveys (n=62) indicated 92% were “highly satisfied” and 100% would recommend the SAC. Conclusions: The SAC reduced ED visits after posterior decompression surgery by over 50%, with pre-operative education, focused QI initiatives, and its individualized, proactive approach.
Actuaries must model mortality to understand, manage and price risk. Continuous-time methods offer considerable practical benefits to actuaries analysing portfolio mortality experience. This paper discusses six categories of advantage: (i) reflecting the reality of data produced by everyday business practices, (ii) modelling rapid changes in risk, (iii) modelling time- and duration-varying risk, (iv) competing risks, (v) data-quality checking and (vi) management information. Specific examples are given where continuous-time models are more useful in practice than discrete-time models.
We reprise some common statistical models for actuarial mortality analysis using grouped counts. We then discuss the benefits of building mortality models from the most elementary items. This has two facets. First, models are better based on the mortality of individuals, rather than groups. Second, models are better defined in continuous time, rather than over fixed intervals like a year. We show how Poisson-like likelihoods at the “macro” level are built up by product integration of sequences of infinitesimal Bernoulli trials at the “micro” level. Observed data is represented through a stochastic mortality hazard rate, and counting processes provide the natural notation for left-truncated and right-censored actuarial data, individual or age-grouped. Together these explain the “pseudo-Poisson” behaviour of survival model likelihoods.
Water hyacinth is a highly invasive aquatic species in the southern United States that requires intensive management through frequent herbicide applications. Quantifying management success in large-scale operations is challenging with traditional survey methods that rely on boat-based teams and can be time-consuming and labor-intensive. In contrast, an unmanned aerial system (UAS) allows a single operator to survey a waterbody more efficiently and rapidly, enhancing both coverage and data collection. Therefore, the objective of this research was to develop remote sensing techniques to assess herbicide efficacy for water hyacinth control in an outdoor mesocosm study. Experiments were conducted in spring and summer 2023 to compare and correlate data from visual evaluations of herbicide efficacy against nine vegetation indices (VIs) derived from UAS-based red-green-blue imagery. Penoxsulam, carfentrazone, diquat, 2,4-D, florpyrauxifen-benzyl, and glyphosate were applied at two rates, and experimental units were evaluated for 6 wk. The carotenoid reflectance index (CRI) had the highest Spearman’s correlation coefficient with visually evaluated efficacy for 2,4-D, diquat, and florpyrauxifen benzyl (> −0.77). The visible atmospherically resistance index (VARI) had the highest correlation with carfentrazone and penoxsulam treatments (> −0.70), and the excess greenness minus redness index had the highest correlation for glyphosate treatments (> −0.83). CRI had the highest correlation coefficient with the most herbicide treatments, and it was the only VI tested that did not include the red band. These VIs were satisfactory predictors of mid-range visually evaluated herbicide efficacy values but were poorly correlated with extremely low and high values, corresponding to nontreated and necrotic plants. Future research should focus on applying findings to real-world (nonexperimental) field conditions and testing imagery with spectral bands beyond the visible range.
We consider pricing of a specialised critical illness and life insurance contract for breast cancer (BC) risk. We compare (a) an industry-based Markov model with (b) a recently developed semi-Markov model, which accounts for unobserved BC cases and progression through clinical stages of BC, and (c) an alternative Markov model derived from (b). All models are calibrated using population data in England and data from the medical literature. We show that the semi-Markov model aligns best with empirical evidence. We then consider net premiums of specialized life insurance products under various scenarios of cancer diagnosis and treatment. The results show strong dependence on the time spent with diagnosed or undiagnosed pre-metastatic BC. This proves to be significant for refining cancer survival estimates and accurately estimating related age dependence by cancer stage. In contrast, the industry-based model, by overlooking this critical factor, is more sensitive to the model assumptions, underscoring its limitations in cancer estimates.
Clinical trials often struggle to recruit enough participants, with only 10% of eligible patients enrolling. This is concerning for conditions like stroke, where timely decision-making is crucial. Frontline clinicians typically screen patients manually, but this approach can be overwhelming and lead to many eligible patients being overlooked.
Methods:
To address the problem of efficient and inclusive screening for trials, we developed a matching algorithm using imaging and clinical variables gathered as part of the AcT trial (NCT03889249) to automatically screen patients by matching these variables with the trials’ inclusion and exclusion criteria using rule-based logic. We then used the algorithm to identify patients who could have been enrolled in six trials: EASI-TOC (NCT04261478), CATIS-ICAD (NCT04142125), CONVINCE (NCT02898610), TEMPO-2 (NCT02398656), ESCAPE-MEVO (NCT05151172), and ENDOLOW (NCT04167527). To evaluate our algorithm, we compared our findings to the number of enrollments achieved without using a matching algorithm. The algorithm’s performance was validated by comparing results with ground truth from a manual review of two clinicians. The algorithm’s ability to reduce screening time was assessed by comparing it with the average time used by study clinicians.
Results:
The algorithm identified more potentially eligible study candidates than the number of participants enrolled. It also showed over 90% sensitivity and specificity for all trials, and reducing screening time by over 100-fold.
Conclusions:
Automated matching algorithms can help clinicians quickly identify eligible patients and reduce resources needed for enrolment. Additionally, the algorithm can be modified for use in other trials and diseases.
Benghal dayflower and sicklepod are weeds of economic importance in peanut in the southeastern United States due to their extended emergence pattern and limited effective herbicides for control. Field studies were conducted near Jay, Florida, in 2022 and 2023, to evaluate the effect of planting date and herbicide combinations on Benghal dayflower and sicklepod control in peanut crops. Peanut planted in June was exposed to a higher Benghal dayflower density than peanut planted in May. Sicklepod density was similar between May and June planting dates at 28 d after preemergence and early postemergence herbicide applications, but density was greater in peanut that was planted in June, 28 d after the mid-postemergence application. A preemeergence herbicide application followed by (fb) an early postemergence application of S-metolachlor or diclosulam + S-metolachlor controlled Benghal dayflower 84% to 93% 28 d after early postemergence in peanut that was planted in May, but control was reduced to 58% to 78% in the crop that had been planted in June. Regardless of planting date, a preemeergence application fb S-metolachlor or diclosulam + S-metolachlor applied early postemergence provided <80% sicklepod control 28 d after early postemergence. Imazapic + dimethenamid-P + 2,4-DB applied postemergence improved Benghal dayflower control to at least 94% 28 d after mid-postemergence, but sicklepod control was not >85%. Regardless of the planting date, paraquat + bentazon + S-metolachlor applied early postemergence was required to achieve ≥95% sicklepod control. However, herbicide combinations that included paraquat + bentazon + S-metolachlor reduced peanut yield when planting was delayed to June. In fields that are infested with Benghal dayflower and sicklepod, it is recommended that peanut be planted in early May to minimize the potential impact of these weeds and to increase peanut yield. Late-planted peanut required more intensive herbicide applications to obtain the same peanut yield as the May-planted peanut.
Sicklepod is one of the most difficult to control weeds in peanut production in the southeastern United States due to its extended emergence pattern and limited effective herbicides for control. Growers rely on preemergence herbicides as the foundation of their weed control programs; however, postemergence herbicides are often needed for season-long weed control. The objectives of this study were to evaluate the effect of planting pattern and herbicide combinations for sicklepod control in peanut crops. Due to rapid canopy closure, twin-row planting improved late-season sicklepod control by 13% and peanut yield by 5% compared with a single-row pattern. A preemergence application of fluridone, flumioxazin, or fluridone + flumioxazin provided 76% to 89% control of sicklepod 28 d after preemergence. Regardless of the herbicide applied preemergence, paraquat + bentazon + S-metolachlor applied early postemergence was required to achieve ≥90% sicklepod control 28 d after early postemergence. All preemergence herbicide treatments followed by (fb) S-metolachlor or diclosulam + S-metolachlor applied early postemergence provided <90% control 28 d after early postemergence. A mid-postemergence application of imazapic + dimethenamid-P + 2,4-DB controlled sicklepod by 67% to 79% prior to peanut harvest, and biomass reduction was unacceptable (<80%), resulting in difficulty in peanut digging. The highest peanut yield was observed when paraquat + bentazon + S-metolachlor was applied early postemergence fb imazapic + dimethenamid-P + 2,4-DB applied mid-postemergence. Based on the results of this study, a herbicide combination of paraquat + bentazon + S-metolachlor is an important early-season tool for controlling sicklepod in peanut crops. The results also showed that a twin-row planting pattern improved late-season sicklepod control but did not reduce herbicide input to protect peanut yield.
Background: Currently there are no disease modifying treatment for Synucleinopathies including Parkinson’s disease Dementia (PDD). Carrying a mutation in the GBA gene (beta-glucocerebrosidase/ GCAse) is a leading risk factor for synucleinopathies. Raising activity GCAse lowers α-synuclein levels in cells and animal models. Ambroxol is a pharmacological chaperone for GCAse and can raise GCAse levels. Our goal is to test Ambroxol as a disease-modifying treatment in PDD. Methods: We randomized fifty-five individuals with PDD to Ambroxol 1050mg/day, 525mg/day, or placebo for 52 weeks. Primary outcome measures included safety, Alzheimer’s disease Assessment Scale-cognitive (ADAS-Cog) subscale and the Clinician’s Global Impression of Change (CGIC). Secondary outcomes included pharmacokinetics, cognitive and motor outcomes and and plasma and CSF biomarkers. Results: Ambroxol was well tolerated. There were 7 serious adverse events (SAEs) none deemed related to Ambroxol. GCase activity was increased in white blood cells by ~1.5 fold. There were no differences between groups on primary outcome measures. Patients receiving high dose Ambroxol appeared better on the Neuropsychiatric Inventory. GBA carriers appeared to improve on some cognitive tests. pTau 181 was reduced in CSF. Conclusions: Ambroxol was safe and well-tolerated in PDD. Ambroxol may improve biomarkers and cognitive outcomes in GBA1 mutation carrie.rs Ambroxol improved some biomarkerss. ClinicalTrials.gov NCT02914366
In this paper, we construct interpretable zero-inflated neural network models for modeling hospital admission counts related to respiratory diseases among a health-insured population and their dependants in the United States. In particular, we exemplify our approach by considering the zero-inflated Poisson neural network (ZIPNN), and we follow the combined actuarial neural network (CANN) approach for developing zero-inflated combined actuarial neural network (ZIPCANN) models for modeling admission rates, which can accommodate the excess zero nature of admission counts data. Furthermore, we adopt the LocalGLMnet approach (Richman & Wüthrich (2023). Scandinavian Actuarial Journal, 2023(1), 71–95.) for interpreting the ZIPNN model results. This facilitates the analysis of the impact of a number of socio-demographic factors on the admission rates related to respiratory disease while benefiting from an improved predictive performance. The real-life utility of the methodologies developed as part of this work lies in the fact that they facilitate accurate rate setting, in addition to offering the potential to inform health interventions.
Targeted spraying application technologies have the capacity to drastically reduce herbicide inputs, but to be successful, the performance of both machine vision–based weed detection and actuator efficiency needs to be optimized. This study assessed (1) the performance of spotted spurge recognition in ‘Latitude 36’ bermudagrass turf canopy using the You Only Look Once (YOLOv3) real-time multiobject detection algorithm and (2) the impact of various nozzle densities on model efficiency and projected herbicide reduction under simulated conditions. The YOLOv3 model was trained and validated with a data set of 1,191 images. The simulation design consisted of four grid matrix regimes (3 × 3, 6 × 6, 12 × 12, and 24 × 24), which would then correspond to 3, 6, 12, and 24 nonoverlapping nozzles, respectively, covering a 50-cm-wide band. Simulated efficiency testing was conducted using 50 images containing predictions (labels) generated with the trained YOLO model and by applying each of the grid matrixes to individual images. The model resulted in prediction accuracy of an F1 score of 0.62, precision of 0.65, and a recall value of 0.60. Increased nozzle density (from 3 to 12) improved actuator precision and predicted herbicide-use efficiency with a reduction in the false hits ratio from ∼30% to 5%. The area required to ensure herbicide deposition to all spotted spurge detected within images was reduced to 18%, resulting in ∼80% herbicide savings compared to broadcast application. Slightly greater precision was predicted with 24 nozzles but was not statistically different from the 12-nozzle scenario. Using this turf/weed model as a basis, optimal actuator efficacy and herbicide savings would occur by increasing nozzle density from 1 to 12 nozzles within the context of a single band.
Carrier water quality is an important consideration for herbicide efficacy. Field and greenhouse studies were conducted from 2021 to 2023 to evaluate the effect of carrier water pH and hardness on imazapic efficacy for sicklepod control in peanut crops. In separate field experiments imazapic was applied postemergence at 0.071 kg ai ha−1 with carrier water pH levels of 5, 6, 7, 8, or 9; and hardness levels of 0 (deionized water), 100, 200, 400, or 500 mg L−1 of CaCO3 equivalent. In greenhouse experiments, imazapic was applied to sicklepod that was either 10 cm, 15 cm, or 20 cm tall at similar carrier water pH levels and hardness levels of 0, 100, 200, 400, or 800 mg L−1 of CaCO3. In the field study, sicklepod control, density, and biomass reductions were lower with carrier water pH 5 or 9 compared with pH 7. In the greenhouse study, control was not different among carrier water pH levels when imazapic was applied to 10-cm-tall sicklepod; however, when applied to 15- or 20-cm-tall sicklepod, control was at least 25% greater with acidic (pH 5) compared to alkaline (pH 9) carrier water. Results from the field study showed that carrier water hardness ≤500 ppm did not reduce the efficacy of imazapic to control sicklepod. In the greenhouse study, regardless of sicklepod height, carrier water hardness of 800 mg L−1 reduced sicklepod control by 15% and biomass reduction by 17% compared with deionized water (pH 7). The effects of carrier water pH and hardness on imazapic efficacy did not compromise peanut yield in the field study. However, this study indicates that both acidic and alkaline carrier water pH and hardness (800 mg L−1 CaCO3 L−1) have the potential to reduce imazapic efficacy on sicklepod, and appropriate spray solution amendments maybe be needed to maintain optimum efficacy.
Herbicides are the primary tool for controlling weeds in peanut (Arachis hypogaea L.) and are crucial to sustainable peanut production in the United States. The literature on chemical weed management in peanut in the past 53 yr (1970 to 2022) in the United States was systematically reviewed to highlight the strengths and weaknesses of different herbicides and identify current research gaps in chemical weed management. Residual weed control in peanut is achieved mainly with dimethenamid-P, ethalfluralin, pendimethalin, and S-metolachlor. More recently, the use of the protoporphyrinogen oxidase inhibitor flumioxazin and acetolactate synthase inhibitors, such as diclosulam, for residual weed control in peanut has increased considerably. Postemergence broadleaf weed control in peanut is achieved mainly with acifluorfen, bentazon, diclosulam, imazapic, lactofen, paraquat, and 2,4-DB, while the graminicides clethodim and sethoxydim are the major postemergence grass weed control herbicides in peanut. Although several herbicides are available for weed control in peanut, no single herbicide can provide season-long weed control due to limited application timing, lack of extended residual activity, variability in weed control spectrum, and rotational restrictions. Therefore, effective weed management in peanut often requires herbicide mixtures and/or sequential application of preplant-incorporated, preemergence, and/or postemergence herbicides. However, the available literature showed a substantive range in herbicide efficacy due to variations in environmental conditions and flushes of weed germination across years and locations. Despite the relatively high efficacy of herbicides, the selection of herbicide-resistant weeds is another area of increasing concern. Future research should focus on developing new strategies for preventing or delaying the development of resistance and improving herbicide efficacy within the context of climate change and emerging constraints such as water shortages, rising temperatures, and increasing CO2 concentration.
Older adults living in residential care often experience challenges in sustaining meaningful social relationships, which can result in compromised health and well-being. Online social networking has the potential to mitigate this problem, but few studies have investigated its implementation and its effectiveness in maintaining or enhancing well-being. This pilot study used a cluster-randomized pre–post design to examine the feasibility of implementing a 12-week group-based technology-training intervention for older adults (n = 48) living in residential care by exploring how cognitive health, mental health, and confidence in technology were impacted. Analysis of variance revealed significant increases in life satisfaction, positive attitudes toward computer use, and self-perceived competence among participants who received the intervention, but increased depressive symptoms for the control group. These findings suggest that, despite challenges in implementing the intervention in residential care, group-based technology training may enhance confidence among older adults while maintaining or enhancing mental health.
Insomnia in depression is common and difficult to resolve. Music is commonly used as a sleep aid, and clinical trials pointing to positive effects of music as a sleep aid are increasing adding to the evidence base. There is little knowledge on the effectiveness of music for depression related insomnia.
Objectives
A recent RCT study conducted in psychiatry at Aalborg University Hospital examined effects of a music intervention for insomnia in depression. The intervention group listened to music at bedtime for four weeks, controls were offered music intervention post-test. Primary outcome measure was Pittsburgh Sleep Quality Index (PSQI). Secondary outcomes included Actigraphy, The Hamilton depression Rating Scale (HAMD-17) and World Health Organisation well-being questionnaires (WHO-5, WHOQOL-BREF).
Methods
A two-armed randomized controlled trial (n=112) and a qualitative interview study (n=4)
Results
The RCT study showed signficant improvements for the music intervention group in sleep quality and quality of life at four weeks according to global PSQI scores (effect size= -2.1, 95%CI -3.3; -0.9) and WHO-5 scores (effect size 8.4, 95%CI 2.7; 14.0). Actigraphy measures showed no changes and changes in depression symptoms (HAMD-17) were not detected.
The interview study unfolded examples of the influences of music on sleep and relaxation. Music distracted, affected mood and arousal positively and supported formation of sleep habits.
Results from the trial are discussed and merged with findings from the interview study. The results from the trial suggested moderate effects of music listening for the population while findings from the interview study showed examples of individual and highly varying outcomes.
Conclusions
Music is suggested as a low-cost, side-effect free and safe intervention in supplement to existing treatments improving sleep in depression.
Weed interference is a major factor that reduces peanut (Arachis hypogaea L.) yield in the United States. Peanut growers rely heavily on herbicides for weed control. Although effective, herbicides are not a complete solution to the complex challenge that weeds present. Therefore, the use of nonchemical weed management options is essential. The literature on weed research in peanut in the past 53 yr in the United States was reviewed to assess the achievements and identify current research gaps and prospects for nonchemical weed management for future research. More than half (79%) of the published studies were from the southeastern United States. Most studies (88%) focused on weed management, while fewer studies (12%) addressed weed distribution, ecology, and competitive mechanisms. Broadleaf weeds were the most frequently studied weed species (60%), whereas only 23% and 19% of the published studies were relevant to grasses and Cyperus spp., respectively. Seventy-two percent of the published studies focused on curative measures using herbicides. Nonchemical methods using mechanical (5%) and preventive (13%) measures that influence crop competition and reduce the buildup of the weed seedbank, seedling recruitment, and weed seed production have received less attention. In most studies, the preventive weed management measures provided weed suppression and reduced weed competition but were not effective enough to reduce the need for herbicides to protect peanut yield. Therefore, future research should focus on developing integrated weed management strategies based on multiple preventive measures rather than one preventive measure combined with one or more curative measures. We recommend that research on mechanical weed management should focus on the role of cultivation when integrated with currently available herbicides. For successful weed management with lasting outcomes, the dominant weed communities of specific target locations should be addressed within the context of climate change and emerging constraints rather than focusing on single problematic species.
Over the past fifteen years, a narrative has developed that IR scholars have become a “cult of the irrelevant,” with declining influence on and engagement with policy debates. Despite these assertions, the evidence for limited policy engagement has been anecdotal. We investigate the extent of policy engagement—the ways in which IR scholars participate in policy-making processes and/or attempt to shape those processes—by surveying IR scholars directly about their engagement activities. We find policy engagement is pervasive among IR scholars. We draw on theories of credit-claiming to motivate expectations about how and when scholars are likely to engage with practitioners. Consistent with our expectations, much of this engagement comes in forms that involve small time commitments and provide opportunities for credit-claiming, such as media appearances and short-form, bylined op-eds and blog posts. However, sizable minorities report engaging in consulting activities not for attribution/publication and writing policy briefs, and a majority of respondents indicate they engaged in these activities several times a year or more. We find only small differences in engagement across gender and rank. Our results demonstrate that, for IR scholars, some form of policy engagement is the norm.
To evaluate the impact of implementing clinical decision support (CDS) tools for outpatient antibiotic prescribing in the emergency department (ED) and clinic settings.
Design:
We performed a before-and-after, quasi-experimental study that employed an interrupted time-series analysis.
Setting:
The study institution was a quaternary, academic referral center in Northern California.
Participants:
We included prescriptions for patients in the ED and 21 primary-care clinics within the same health system.
Intervention:
We implemented a CDS tool for azithromycin on March 1, 2020, and a CDS tool for fluoroquinolones (FQs; ie, ciprofloxacin, levofloxacin, and moxifloxacin) on November 1, 2020. The CDS added friction to inappropriate ordering workflows while adding health information technology (HIT) features to easily perform recommended actions. The primary outcome was the number of monthly prescriptions for each antibiotic type, by implementation period (before vs after).
Results:
Immediately after azithromycin-CDS implementation, monthly rates of azithromycin prescribing decreased significantly in both the ED (−24%; 95% CI, −37% to −10%; P < .001) and outpatient clinics (−47%; 95% CI, −56% to −37%; P < .001). In the first month after FQ-CDS implementation in the clinics, there was no significant drop in ciprofloxacin prescriptions; however, there was a significant decrease in ciprofloxacin prescriptions over time (−5% per month; 95% CI, −6% to −3%; P < .001), suggesting a delayed effect of the CDS.
Conclusion:
Implementing CDS tools was associated with an immediate decrease in azithromycin prescriptions, in both the ED and clinics. CDS may serve as a valuable adjunct to existing antimicrobial stewardship programs.