Decision analysis of PPP project's parties based on deep consumer participation.

Although PPP(Public-private partnership) mode has been applied for a long time in infrastructural project, the success rate is not very high. The sustainability of PPP projects is still influenced by many factors. In order to examine the evolutionary stable strategies (ESSs) of social capital, government, and paying consumers, a tripartite evolutionary game model is established in this work. In order to further promote consumer participation, it is necessary to make the assumption that customer oversight and review can have an impact on service prices. The results show: i)The strategy choice of consumer depends on the comparison between supervision cost of consumer and price coefficient for consumer to social capital. ii)Consumer supervision can promote the provision of high-quality services by social capital. iii)The difference between high-quality cost and low-quality cost, subsidy coefficient, price coefficient and supervision cost of consumer are critical factors influencing both evolutionary results and trajectories. This paper also puts forward policy implications for the three stakeholders to promote social capital's high-quality strategy so as to maintain the sustainability of PPP projects.

Design of a multiple criteria decision analysis framework for prioritizing high-impact health technologies in a regional health service.

OBJECTIVES: This study aims to develop a framework for establishing priorities in the regional health service of Murcia, Spain, to facilitate the creation of a comprehensive multiple criteria decision analysis (MCDA) framework. This framework will aid in decision-making processes related to the assessment, reimbursement, and utilization of high-impact health technologies. METHOD: Based on the results of a review of existing frameworks for MCDA of health technologies, a set of criteria was proposed to be used in the context of evaluating high-impact health technologies. Key stakeholders within regional healthcare services, including clinical leaders and management personnel, participated in a focus group (n = 11) to discuss the proposed criteria and select the final fifteen. To elicit the weights of the criteria, two surveys were administered, one to a small sample of healthcare professionals (n = 35) and another to a larger representative sample of the general population (n = 494). RESULTS: The responses obtained from health professionals in the weighting procedure exhibited greater consistency compared to those provided by the general public. The criteria more highly weighted were "Need for intervention" and "Intervention outcomes." The weights finally assigned to each item in the multicriteria framework were derived as the equal-weighted sum of the mean weights from the two samples. CONCLUSIONS: A multi-attribute function capable of generating a composite measure (multicriteria) to assess the value of high-impact health interventions has been developed. Furthermore, it is recommended to pilot this procedure in a specific decision context to evaluate the efficacy, feasibility, usefulness, and reliability of the proposed tool.

Challenges and Opportunities in Interdisciplinary Research and Real-World Data for Treatment Sequences in Health Technology Assessments.

With an ever-increasing number of treatment options, the assessment of treatment sequences has become crucial in health technology assessment (HTA). This review systematically explores the multifaceted challenges inherent in evaluating sequences, delving into their interplay and nuances that go beyond economic model structures. We synthesised a 'roadmap' of literature from key methodological studies, highlighting the evolution of recent advances and emerging research themes. These insights were compared against HTA guidelines to identify potential avenues for future research. Our findings reveal a spectrum of challenges in sequence evaluation, encompassing selecting appropriate decision-analytic modelling approaches and comparators, deriving appropriate clinical effectiveness evidence in the face of data scarcity, scrutinising effectiveness assumptions and statistical adjustments, considering treatment displacement, and optimising model computations. Integrating methodologies from diverse disciplines-statistics, epidemiology, causal inference, operational research and computer science-has demonstrated promise in addressing these challenges. An updated review of application studies is warranted to provide detailed insights into the extent and manner in which these methodologies have been implemented. Data scarcity on the effectiveness of treatment sequences emerged as a dominant concern, especially because treatment sequences are rarely compared in clinical trials. Real-world data (RWD) provide an alternative means for capturing evidence on effectiveness and future research should prioritise harnessing causal inference methods, particularly Target Trial Emulation, to evaluate treatment sequence effectiveness using RWD. This approach is also adaptable for analysing trials harbouring sequencing information and adjusting indirect comparisons when collating evidence from heterogeneous sources. Such investigative efforts could lend support to reviews of HTA recommendations and contribute to synthesising external control arms involving treatment sequences.

Anesthesia decision analysis using a cloud-based big data platform.

Big data technologies have proliferated since the dawn of the cloud-computing era. Traditional data storage, extraction, transformation, and analysis technologies have thus become unsuitable for the large volume, diversity, high processing speed, and low value density of big data in medical strategies, which require the development of novel big data application technologies. In this regard, we investigated the most recent big data platform breakthroughs in anesthesiology and designed an anesthesia decision model based on a cloud system for storing and analyzing massive amounts of data from anesthetic records. The presented Anesthesia Decision Analysis Platform performs distributed computing on medical records via several programming tools, and provides services such as keyword search, data filtering, and basic statistics to reduce inaccurate and subjective judgments by decision-makers. Importantly, it can potentially to improve anesthetic strategy and create individualized anesthesia decisions, lowering the likelihood of perioperative complications.

Decision analysis in cardiac surgery: a scoping review and methodological primer.

OBJECTIVES: Randomized controlled trials are the gold standard for evidence generation in medicine but are limited by their real-world generalizability, resource needs, shorter follow-up durations and inability to be conducted for all clinical questions. Decision analysis (DA) models may simulate trials and observational studies by using existing data and evidence- and expert-informed assumptions and extend analyses over longer time horizons, different study populations and specific scenarios, helping to translate population outcomes to patient-specific clinical and economic outcomes. Here, we present a scoping review and methodological primer on DA for cardiac surgery research. METHODS: A scoping review was performed using the PubMed/MEDLINE, EMBASE and Web of Science databases for cardiac surgery DA studies published until December 2021. Articles were summarized descriptively to quantify trends and ascertain methodological consistency. RESULTS: A total of 184 articles were identified, among which Markov models (N = 92, 50.0%) were the most commonly used models. The most common outcomes were costs (N = 107, 58.2%), quality-adjusted life-years (N = 96, 52.2%) and incremental cost-effectiveness ratios (N = 89, 48.4%). Most (N = 165, 89.7%) articles applied sensitivity analyses, most frequently in the form of deterministic sensitivity analyses (N = 128, 69.6%). Reporting of guidelines to inform the model development and/or reporting was present in 22.3% of articles. CONCLUSION: DA methods are increasing but remain limited and highly variable in cardiac surgery. A methodological primer is presented and may provide researchers with the foundation to start with or improve DA, as well as provide readers and reviewers with the fundamental concepts to review DA studies.

Guía metodológica para la estimación y la proyección de muy corto plazo del gasto en salud con base en las clasificaciones del SHA2011: Informe final

La oportunidad es un principio básico de la calidad de los datos de gasto que permite acceder a la información cuando se necesita. Elaborar un presupuesto anual y detectar necesidades de ajuste de una política en marcha o una desviación del gasto respecto de su uso esperado son ejemplos de los beneficios de utilizar los resultados de una cuenta de salud actual y oportuna. Implica, también, realizar cuentas de salud todos los años y presentar los resultados en un plazo breve. Si bien es cierto que la exactitud es también una condición de la calidad que puede hacer necesario esperar por resultados auditados o una búsqueda más exhaustiva de datos, cuando no se requiere de estadísticas históricas, la oportunidad cobra relevancia. En la actualidad se utilizan datos contemporáneos aun cuando estos se enriquezcan del análisis del pasado y de las tendencias. Este documento apoya la estimación temprana de gasto en las cuentas de salud para asegurar su oportunidad. En caso necesario, se puede compensar el rezago en informes que se utilizan para la generación de las cuentas con proyecciones de corto plazo. ¿Cuáles son las opciones contables para ello? ¿Cuáles procedimientos se recomiendan como usuales para asegurar que los resultados del estándar sean oportunos? Se espera que las cifras reales reemplacen a las proyecciones de corto plazo tan pronto como las fuentes de información estén accesibles; sin embargo, los resultados de las proyecciones ejemplificadas en este documento permiten asegurar que los valores “esperados” pueden contribuir a la toma de decisiones informada.

Implementing decision aids for cardiovascular disease prevention: stakeholder interviews and case studies in Australian primary care.

BACKGROUND: Australian cardiovascular disease (CVD) prevention guidelines recommend absolute CVD risk assessment, but less than half of eligible patients have the required risk factors recorded due to fragmented implementation over the last decade. Co-designed decision aids for general practitioners (GPs) and consumers have been developed that improve knowledge barriers to guideline-recommended CVD risk assessment and management. This study used a stakeholder consultation process to identify and pilot test the feasibility of implementation strategies for these decision aids in Australian primary care. METHODS: This mixed methods study included: (1) stakeholder consultation to map existing implementation strategies (2018-20); (2) interviews with 29 Primary Health Network (PHN) staff from all Australian states and territories to identify new implementation opportunities (2021); (3) pilot testing the feasibility of low, medium, and high resource implementation strategies (2019-21). Framework Analysis was used for qualitative data and Google analytics provided decision support usage data over time. RESULTS: Informal stakeholder discussions indicated a need to partner with existing programs delivered by the Heart Foundation and PHNs. PHN interviews identified the importance of linking decision aids with GP education resources, quality improvement activities, and consumer-focused prevention programs. Participants highlighted the importance of integration with general practice processes, such as business models, workflows, medical records and clinical audit software. Specific implementation strategies were identified as feasible to pilot during COVID-19: (1) low resource: adding website links to local health area guidelines for clinicians and a Heart Foundation toolkit for primary care providers; (2) medium resource: presenting at GP education conferences and integrating the resources into audit and feedback reports; (3) high resource: auto-populate the risk assessment and decision aids from patient records via clinical audit software. CONCLUSIONS: This research identified a wide range of feasible strategies to implement decision aids for CVD risk assessment and management. The findings will inform the translation of new CVD guidelines in primary care. Future research will use economic evaluation to explore the added value of higher versus lower resource implementation strategies.

Spatial multi-criteria decision analysis for the selection of sentinel regions in tick-borne disease surveillance.

BACKGROUND: The implementation of cost-effective surveillance systems is essential for tracking the emerging risk of tick-borne diseases. In Canada, where Lyme disease is a growing public health concern, a national sentinel surveillance network was designed to follow the epidemiological portrait of this tick-borne disease across the country. The surveillance network consists of sentinel regions, with active drag sampling carried out annually in all regions to assess the density of Ixodes spp. ticks and prevalence of various tick-borne pathogens in the tick population. The aim of the present study was to prioritize sentinel regions by integrating different spatial criteria relevant to the surveillance goals. METHODS: We used spatially-explicit multi-criteria decision analyses (MCDA) to map priority areas for surveillance across Canada, and to evaluate different scenarios using sensitivity analyses. Results were shared with stakeholders to support their decision making for the selection of priority areas to survey during active surveillance activities. RESULTS: Weights attributed to criteria by decision-makers were overall consistent. Sensitivity analyses showed that the population criterion had the most impact on rankings. Thirty-seven sentinel regions were identified across Canada using this systematic and transparent approach. CONCLUSION: This novel application of spatial MCDA to surveillance network design favors inclusivity of nationwide partners. We propose that such an approach can support the standardized planning of spatial design of sentinel surveillance not only for vector-borne disease BDs, but more broadly for infectious disease surveillance where spatial design is an important component.

Combination chemotherapy with trastuzumab in early-stage breast cancer: a meta-analysis and Bayesian decision analysis of different treatment regimens.

OBJECTIVE: HER2-positive breast cancer is a high-risk malignant tumor, and trastuzumab is an effective targeted therapy drug, but its optimal duration remains uncertain. To compare the efficacy and cost-effectiveness of different durations (6 months, 9 months, 12 months, and 18 months) of trastuzumab combined with chemotherapy in patients with early breast cancer by meta-analysis and Bayesian decision analysis. PATIENTS AND METHODS: Randomized controlled trials comparing the effectiveness of different durations of trastuzumab combination chemotherapy in early-stage breast cancer patients were collected by searching multiple databases. Data synthesis was performed using the R software, and a decision tree model was constructed to simulate the expected outcomes and anticipated costs associated with different treatment durations. RESULTS: This study included 9 randomized controlled trials involving 11,328 early-stage breast cancer patients. The meta-analysis results demonstrated that, compared to the control group, trastuzumab combination chemotherapy at different durations significantly improved disease-free survival and overall survival in early-stage breast cancer patients. Among the various treatment durations, it was observed that 12 months of trastuzumab combination chemotherapy, in comparison to other durations, significantly reduced the risk of recurrence and mortality in early-stage breast cancer patients while maintaining a favorable cost-effectiveness ratio. Bayesian decision analysis also confirmed that 12 months of trastuzumab combination chemotherapy is the optimal treatment duration. CONCLUSIONS: It is recommended to use 12 months of trastuzumab combination chemotherapy as the standard treatment for early-stage breast cancer patients.

Decision analysis framework for predicting no-shows to appointments using machine learning algorithms.

BACKGROUND: No-show to medical appointments has significant adverse effects on healthcare systems and their clients. Using machine learning to predict no-shows allows managers to implement strategies such as overbooking and reminders targeting patients most likely to miss appointments, optimizing the use of resources. METHODS: In this study, we proposed a detailed analytical framework for predicting no-shows while addressing imbalanced datasets. The framework includes a novel use of z-fold cross-validation performed twice during the modeling process to improve model robustness and generalization. We also introduce Symbolic Regression (SR) as a classification algorithm and Instance Hardness Threshold (IHT) as a resampling technique and compared their performance with that of other classification algorithms, such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), and resampling techniques, such as Random under Sampling (RUS), Synthetic Minority Oversampling Technique (SMOTE) and NearMiss-1. We validated the framework using two attendance datasets from Brazilian hospitals with no-show rates of 6.65% and 19.03%. RESULTS: From the academic perspective, our study is the first to propose using SR and IHT to predict the no-show of patients. Our findings indicate that SR and IHT presented superior performances compared to other techniques, particularly IHT, which excelled when combined with all classification algorithms and led to low variability in performance metrics results. Our results also outperformed sensitivity outcomes reported in the literature, with values above 0.94 for both datasets. CONCLUSION: This is the first study to use SR and IHT methods to predict patient no-shows and the first to propose performing z-fold cross-validation twice. Our study highlights the importance of avoiding relying on few validation runs for imbalanced datasets as it may lead to biased results and inadequate analysis of the generalization and stability of the models obtained during the training stage.

GIS-based land suitability analysis for the optimal location of integrated multi-trophic aquaponic systems.

Aquaponics has witnessed global proliferation and a notable enhancement in sustainability in recent years. Consequently, it assumes paramount importance to delineate optimal locations for its implementation, in fact, the success of an aquaponic facility also depends on its geographical placement, necessitating consideration of many variables encompassing natural resources, socioeconomic factors, infrastructural availability and environmental constraints, whether natural or artificial. This paper focuses on the definition and test in the Emilia-Romagna region (Italy) of a GIS-based multi-criteria land suitability assessment model aimed at allowing the diffusion and environmental integration of innovative integrated multi-trophic aquaponic systems. The process has been implemented with a Weighted Linear Combination (WLC) model, where decisions and criteria were selected via a participatory mechanism involving experts in various fields. The region has been subdivided into 50 × 50 m grid cells, with each grid cell being associated with a value ranging from 0 to 8. In this context, a rating of 0 means unsuitability, while a rating of 1 denotes minimal suitability, and the highest rating of 8 designates maximal suitability. Notably, a substantial portion of the surveyed territory has been found to be completely unsuitable for the establishment of aquaponic facilities. More than 86.4% of the remaining suitable areas were rated 6, 7, or 8, affirming the overall favourability of the Emilia-Romagna region for aquaponic installations. Finally, the veracity and robustness of the results have been tested through a one-at-a-time sensitivity analysis, that has proven the appropriateness of the proposed model.

Evaluation of antibody-based preventive alternatives for respiratory syncytial virus: a novel multi-criteria decision analysis framework and assessment of nirsevimab in Spain.

BACKGROUND: Respiratory syncytial virus (RSV) is a highly infectious disease that poses a significant clinical and medical burden, as well as social disruption and economic costs, recognized by the World Health Organization as a public health issue. After several failed attempts to find preventive candidates (compounds, products, including vaccines), new alternatives might be available, one being nirsevimab, the first and only option approved for RSV prevention in neonates and infants during their first RSV season. The objective of this study was to develop a novel multi-criteria decision analysis (MCDA) framework for RSV antibody-based preventive alternatives and to use it to assess the value of nirsevimab vs. placebo as a systematic immunization approach to prevent RSV in neonates and infants during their first RSV season in Spain. METHODS: Based on a pre-established model called Vaccinex, an ad-hoc MCDA framework was created to reflect relevant attributes for the assessment of current and future antibody-based preventive measures for RSV. The estimated value of nirsevimab was obtained by means of an additive linear model combining weights and scores assigned by a multidisciplinary committee of 9 experts. A retest and three sensitivity analyses were conducted. RESULTS: Nirsevimab was evaluated through a novel framework with 26 criteria by the committee as a measure that adds value (positive final estimated value: 0.56 ± 0.11) to the current RSV scenario in Spain, by providing a high efficacy for prevention of neonates and infants. In addition, its implementation might generate cost savings in hospitalizations and to the healthcare system and increase the level of public health awareness among the general population, while reducing health inequities. CONCLUSIONS: Under a methodology with increasing use in the health field, nirsevimab has been evaluated as a measure which adds value for RSV prevention in neonates and infants during their first RSV season in Spain.

A video-based decision aid improved knowledge and decreased decisional conflict for asymptomatic persistent endodontic lesions.

AIM: This prospective cohort study aimed to (i) evaluate patients' treatment decisions, decisional conflict and knowledge levels before and after use of a video-based decision aid (DA) on asymptomatic persistent endodontic lesions (APEL) and to (ii) elicit important decision factors for patients in the decision-making process. METHODOLOGY: A video DA modelled after the International Patient Decision Aids Standards (IPDAS) was designed. The video was pilot-tested, revised and used on 50 patients referred to the graduate endodontic clinic and presenting with an incidental finding of APEL detected on radiographs, with informed consent (DSRB 2020/00133). Recruitment was conducted from June 2020 to November 2021. Patients' treatment decisions and decision-making process were studied. Patients also completed a questionnaire on knowledge gain, decisional conflict and confidence levels. RESULTS: Forty-three patients (48 teeth) out of 50 patients chose long-term monitoring of APEL while 7 patients (7 teeth) decided on endodontic intervention. Median knowledge scores increased from 58.3 (IQR: 41.7, 75.0) to 87.5 (IQR: 75.0, 100.0) (p < .001); median decisional conflict scale (DCS) scores decreased from 25.0 (IQR: 12.5, 34.4) to 3.1 (IQR: 0, 23.8) (p < .001), and 44 (88%) patients were confident or very confident with their treatment decisions. Statistical analyses showed that asymptomatic condition of tooth was the sole decision factor with statistical significance while socioeconomic factors (age, gender, race, education and income levels) showed no significant trend. CONCLUSIONS: The majority of patients presenting with APEL in this study chose long-term monitoring over immediate intervention. Using the video-based DA in the decision-making process significantly improved patients' knowledge and decreased their decisional conflict levels.

A Decision Aid to Support Vocational Rehabilitation Professionals Offering Tailored Care to Benefit Recipients with a Long-Term Work Disability: A Feasibility Study.

PURPOSE: This feasibility study focusses on the implementation and use of a decision aid, which supports vocational rehabilitation (VR) professionals in helping clients with a disability pension return to work in practice. The decision aid shows an overview of the clients' return to work barriers and suggests suitable VR interventions based on these barriers. METHODS: The study population consisted of VR professionals working at the Dutch Social Security Institute and their clients receiving a (partial) work disability pension. The feasibility was measured with concepts of the Linnan and Steckler framework and the attitude, social norm and self-efficacy model. Data were collected using questionnaires, checklists and qualitative interviews. RESULTS: Ten professionals participated in this study. Fifty-four clients were asked to fill in the questionnaire of the decision aid and 32 clients received VR care based on the decision aid. In general, VR professionals and clients were satisfied with the decision aid and perceived a few barriers for using the decision aid. CONCLUSIONS: This study showed that it is feasible to implement and use the decision aid. To improve the implementation of this decision aid, it should be implemented in digital systems used by professionals to improve efficiency of working with the decision aid.

Financial decision analysis based on "willingness to pay" for surgical sperm retrieval approaches among men with non-obstructive azoospermia in the United States.

OBJECTIVE: To determine the most financially optimal surgical approach for testicular sperm retrieval for men with non-obstructive azoospermia. DESIGN: A decision tree was created examining five potential surgical approaches for men with non-obstructive azoospermia pursuing one cycle of intracytoplasmic sperm injection. An expected financial net loss was determined for each surgical option based on couples' willingness to pay for one cycle of intracytoplasmic sperm injection resulting in pregnancy. The branch with the lowest expected net loss was defined as the most optimal financial decision (minimizing loss to a couple). Fresh testicular sperm extraction implied testicular sperm extraction was performed in conjunction with programmed ovulation induction. Frozen testicular sperm extraction implied testicular sperm extraction was performed initially, and ovulation induction/intracytoplasmic sperm injection was canceled if sperm retrieval failed.  The surgical options included fresh conventional testicular sperm extraction, with and without "back-up" sperm cryopreservation, fresh microsurgical testicular sperm extraction, with and without "back-up" sperm cryopreservation, and frozen microsurgical testicular sperm extraction. Success was defined as pregnancy after one intracytoplasmic sperm injection cycle. MATERIALS AND METHODS: Probabilities of successful sperm retrieval with conventional testicular sperm extraction/microsurgical testicular sperm extraction, post-thaw sperm cellular loss following frozen microsurgical testicular sperm extraction, ovulation induction/intracytoplasmic sperm injection cycle out-of-pocket costs, intracytoplasmic sperm injection pregnancy rates for men with non-obstructive azoospermia, standard conventional testicular sperm extraction cost and average willingness to pay for intracytoplasmic sperm injection cycle were gathered from the systematic literature review. Costs were in USD and adjusted to inflation (as of April 2020). Two-way sensitivity analysis was performed on varying couples' willingness to pay for one cycle of intracytoplasmic sperm injection and varying microsurgical testicular sperm extraction out-of-pocket costs. RESULTS: According to our decision tree analysis (assuming minimum microsurgical testicular sperm extraction cost of $1,000 and willingness to pay of $8,000), the expected net loss for each branch was as follows: -$17,545 for fresh conventional testicular sperm extraction, -$17,523 for fresh microsurgical testicular sperm extraction, -$9,624 for frozen microsurgical testicular sperm extraction, -$17,991 for fresh conventional testicular sperm extraction with "backup", and -$18,210 for fresh microsurgical testicular sperm extraction with "backup". Two-way sensitivity analysis with a variable willingness to pay values and microsurgical testicular sperm extraction and in-vitro fertilization costs confirmed that frozen microsurgical testicular sperm extraction consistently presented the lowest net loss compared to other options. Interestingly, when directly comparing fresh microsurgical testicular sperm extraction and conventional testicular sperm extraction with "back-up", scenarios with decreasing willingness to pay and lower microsurgical testicular sperm extraction costs demonstrated fresh conventional testicular sperm extraction with "back-up" as more optimal than fresh microsurgical testicular sperm extraction with "back-up". CONCLUSIONS: For those couples who must pay out of pocket, our study suggests that frozen microsurgical testicular sperm extraction is the most financially optimal decision for the surgical management of non-obstructive azoospermia, regardless of microsurgical testicular sperm extraction cost and the couple's willingness to pay.

Projecting future prolapse outcomes with induction of labor at 39 weeks: a decision analysis.

INTRODUCTION AND HYPOTHESIS: In 2018, the ARRIVE trial (A Randomized Trial of Induction Versus Expectant Management) concluded that routine induction of labor (IOL) at 39 weeks gestation decreases cesarean delivery risk, with slightly lighter birthweight infants. We debated whether routine IOL would improve, worsen, or not change POP risk compared with expectant management (EM). METHODS: We constructed a decision analysis model with a lifetime horizon where nulliparous women reaching 39 weeks underwent IOL or EM. Subsequent vaginal versus cesarean delivery varied based on prior deliveries for up to four births. Subsequent delivery prior to 39 weeks and distribution of gestational age, birthweight, and delivery mode between 24 and 39 weeks was modeled from national data. We modeled increased POP risk with increasing vaginal parity, forceps delivery, and weight of largest infant delivered vaginally, accounting for differential infant weights in each strategy. RESULTS: IOL and EM have similar population-wide POP risk (15.9% and 15.7% respectively). Among women with only spontaneous vaginal deliveries that reached 39 weeks or beyond, the prevalence of POP was 20% after one delivery and 29% after four deliveries, with no difference between groups. The cesarean rate was lower with IOL (27.8% versus 29.8%). Sensitivity analysis revealed no meaningful thresholds among the variables, supporting model robustness. CONCLUSION: While routine induction of labor at 39 weeks results in a meaningfully higher vaginal delivery rate, there was no increase in POP, possibly due to the protective effect of lower birthweight.