Relative Importance Of Items Research Articles

Identifying Perspectives on Physical Therapist Residency Program Goals: Hallmarks of Success and Excellence.

The purpose of this study was to describe common perspectives important to achieving excellence and success in physical therapist residency education programs. Individuals with direct responsibility for creating and revising physical therapist residency program goals participated in a mixed-methods study using Q-methodology. They sorted 31 goal topics based on the level of importance for achieving excellence and success in physical therapist residency education. By-participant factor analysis identified dominant perspectives, which were interpreted based on emergent themes from the topics identified as the most important. Participants' rationale for selecting goal topics as the most important were extracted from the post-survey. Seventy-three individuals responded, representing 9 of the 11 clinical residency areas of practice. Three main perspectives emerged: resident behaviors, resident achievements, and program attributes. One shared perspective emerged across all 3 factors that emphasized the residents' ability to become self-regulated, lifelong learners who integrate learning into practice: preparation for future learning. Variability in weighting the importance of program goals associated with each perspective may include a program's organization and mission, individual beliefs and experiences related to program leadership, and resource availability. Although respondent emphasis placed importance of some items over others, the findings do not suggest that items ranked lower were unnecessary or unimportant in achieving program excellence. The relative importance of items was weighted differently based on the perspective of program leadership and the individual completing the sorting activity. The results of this study suggest that physical therapist residency programs should have at least 1 or 2 goals in each of the 3 distinct perspectives, as well as the 1 shared perspective identified in our findings. Some consistency in program goals based on the perspectives identified here may enable further research exploring excellence, value, and outcomes in physical therapist residency education.

Patient and clinician perspectives on implant dentistry decision aid content: Results from an enhanced Delphi study.

To investigate patient and clinician perspectives on what is considered important to include in a decision aid for replacement of a missing tooth with an implant. An online modified Delphi method with pair comparisons technique was used to survey participants (66 patients, 48 prosthodontists, 46 periodontists, and 31 oral surgeons) in Ontario, Canada from November 2020 to April 2021 regarding the importance of information provided during an implant consultation. Round one included 19 items derived from the literature and informed consent protocols. The decision to retain an item was based on group consensus, defined as at least 75% of participants identifying the item as "important" or "highly important." After analysis of round one results, a second-round survey was sent to all participants to rank the relative importance of the consensus items. Statistical testing was completed using the Kruskal-Wallis one-way analysis of variance test and post hoc Mann-Whitney U tests with a significance level set at p≤0.05. The first and second surveys had response rates of 77.0% and 45.6%, respectively. In round one, all items except purpose of steps reached group consensus. In round two, the highest group ranked items were patient responsibilities for treatment success and follow-ups after treatment. The lowest group ranked items were cost factors and restorative steps. Significant differences between the stakeholder groups were found on several items, including diagnosis (p≤0.00), non-implant options (p≤0.00), and cost (p≤0.01). In general, patients' opinions were significantly different than clinicians' opinions on the relative importance of items. Clinicians and patients feel that multiple items are important to include in a decision aid for implant therapy; however, differences exist between patients and clinicians on the relative importance of items.

Developing the National Usability-Focused Health Information System Scale for Physicians: Validation Study.

BackgroundProblems in the usability of health information systems (HISs) are well acknowledged, but research still lacks a validated questionnaire for measuring and monitoring different dimensions of usability of HISs. Such questionnaires are needed not only for research but also for developing usability of HISs from the viewpoint of end-user experiences.ObjectiveThis study aimed to develop and test the validity of the questionnaire measuring the National Usability-Focused HIS-Scale (NuHISS) among a nationally representative sample of Finnish physicians.MethodsWe utilized 2 cross-sectional data collected from a random sample of Finnish physicians in 2014 (N=3781; of which 2340 [61.9%] were women) and 2017 (N=4018; of which 2604 [64.8%] were women). Exploratory and confirmatory factor analyses (structural equation modeling [SEM]) were applied to test the structural validity of the NuHISS. As the concurrent validity measure, we used the self-reported overall quality of the electronic health record system (school grade) provided by the participants using marginal structural models.ResultsThe exploratory factor analyses with Varimax rotation suggested that the 7-factor solution did offer a good fit to the data in both samples (C2=2136.14 in 2014 and C2=2109.83 in 2017, both P<.001). Moreover, structural equation modelling analyses, using comparative fit index (CFI), Tucker-Lewis Index (TLI), Normed Fit Index (NFI), root mean squared error of approximation (RMSEA), and Standardized Root Mean square Residual (SRMR), showed that the 7-factor solution provided an acceptable fit in both samples (CFI=0.92/0.91, TLI=0.92/0.91, NFI=0.92/0.91, RMSEA=0.048/0.049, and SRMR=0.040/0.039). In addition, concurrent validity of this solution was shown to be acceptable. Ease of use, but also all other dimensions, was especially associated with overall quality reports independent of measured confounders. The 7-factor solution included dimensions of technical quality, information quality, feedback, ease of use, benefits, internal collaboration, and cross-organizational collaboration.ConclusionsNuHISS provides a useful tool for measuring usability of HISs among physicians and offers a valid measure for monitoring the long-term development of HISs on a large scale. The relative importance of items needs to be assessed against national electronic health policy goals and complemented with items that have remained outside the NuHISS from the questionnaire when appropriate.

Nature of Knowledge Technology Across Indian Organizations

Purpose: The primary purpose of this paper is to investigate the nature and prevalence of Knowledge Technology in knowledge organizations and how Knowledge Technology varies across six knowledge organizations on sectoral lines and two genders of knowledge workers from the sample of 204 employees. Design/Methodology/Approach: A questionnaire, containing basic demographic variables and Knowledge Technology items has been provided to respondents i.e., to workers and managers spread across various functional groups, managerial levels from six selected knowledge organizations operating in different sectors of the Indian economy. A total of 204 fully-filled questionnaires have been received both personally and through emails and digital forms. The collected data were statistically treated, using SPSS, with the technique of Principal Component Analysis to figure out the relative importance of items in the Knowledge Technology variable and its unitary nature. Multiple Regression Equation with dummy variables is used to decipher the distribution of Knowledge Technology across six Knowledge Organizations. The difference of perception regarding Knowledge Technology across both genders was assessed using Multiple Regression Equation with dummy variables. Findings: Knowledge Technology is found out to be a unitary variable shown in one Principal Component encompassing 5 technological items having dimensions such as the ability to cater to user idiosyncrasies, contextuality of available knowledge, and intimate connectivity between knowledge and user. The sectoral differences were found and it was evident that the organizations belonging to the IT sector are far ahead in having Knowledge Technology in their organizations. No significant gender differences were found between the perception of male and female knowledge workers regarding their perception of Knowledge Technology in their respective organizations. Research Limitations: This study was conducted in only six knowledge organizations located in India with a sample of 204 knowledge workers. Hence, its generalizability is limited to other similar contexts. The limitations of the questionnaire survey technique are also applicable. Practical Implications: This paper points out that the Knowledge Technology is the unitary variable and the main focus of the Knowledge Technology should be on the accessibility of data, information, and network to an individual user or knowledge worker per se. Even though the organizations operate in different sectors of the economy, efforts should be made such that all the other Indian organizations which are not operating in IT sector, can also benefit from proper deployment of the Knowledge Technology focussing on individual knowledge worker’s needs and requirements being fulfilled at the right time and at the right place for effective knowledge management. Originality/Value: There are very few research studies which focus mainly on the technological dimension in Indian organizations. This empirical research paper tries to fill this gap. This empirical study is all about the Knowledge Technology in Indian organizations and its distribution across gender and sectoral dimensions of the economy.

Nature of Knowledge Technology Across Indian Organizations

Purpose: The primary purpose of this paper is to investigate the nature and prevalence of Knowledge Technology in knowledge organizations and how Knowledge Technology varies across six knowledge organizations on sectoral lines and two genders of knowledge workers from the sample of 204 employees. Design/Methodology/Approach: A questionnaire, containing basic demographic variables and Knowledge Technology items has been provided to respondents i.e., to workers and managers spread across various functional groups, managerial levels from six selected knowledge organizations operating in different sectors of the Indian economy. A total of 204 fully-filled questionnaires have been received both personally and through emails and digital forms. The collected data were statistically treated, using SPSS, with the technique of Principal Component Analysis to figure out the relative importance of items in the Knowledge Technology variable and its unitary nature. Multiple Regression Equation with dummy variables is used to decipher the distribution of Knowledge Technology across six Knowledge Organizations. The difference of perception regarding Knowledge Technology across both genders was assessed using Multiple Regression Equation with dummy variables. Findings: Knowledge Technology is found out to be a unitary variable shown in one Principal Component encompassing 5 technological items having dimensions such as the ability to cater to user idiosyncrasies, contextuality of available knowledge, and intimate connectivity between knowledge and user. The sectoral differences were found and it was evident that the organizations belonging to the IT sector are far ahead in having Knowledge Technology in their organizations. No significant gender differences were found between the perception of male and female knowledge workers regarding their perception of Knowledge Technology in their respective organizations. Research Limitations: This study was conducted in only six knowledge organizations located in India with a sample of 204 knowledge workers. Hence, its generalizability is limited to other similar contexts. The limitations of the questionnaire survey technique are also applicable. Practical Implications: This paper points out that the Knowledge Technology is the unitary variable and the main focus of the Knowledge Technology should be on the accessibility of data, information, and network to an individual user or knowledge worker per se. Even though the organizations operate in different sectors of the economy, efforts should be made such that all the other Indian organizations which are not operating in IT sector, can also benefit from proper deployment of the Knowledge Technology focussing on individual knowledge worker’s needs and requirements being fulfilled at the right time and at the right place for effective knowledge management. Originality/Value: There are very few research studies which focus mainly on the technological dimension in Indian organizations. This empirical research paper tries to fill this gap. This empirical study is all about the Knowledge Technology in Indian organizations and its distribution across gender and sectoral dimensions of the economy.

Efficiently mining frequent itemsets with weight and recency constraints

In the past, a novel framework named recent weighted frequent itemset mining (RWFIM) and two projection-based algorithms, RWFIM-P and RWFIM-PE, were proposed to consider both the relative importance of items (item weights) and the recency of patterns. However, the projection-and-test mechanism used by these algorithms to discover recent weighted frequent itemsets (RWFIs) in a recursive way may have poor performance when the database is dense or contains long transactions. To address this issue, an efficient tree-based RWFI-Mine algorithm is proposed in this paper for mining RWFIs, which considers both weight and the recency of patterns. A novel Set-enumeration tree called the recent weighted frequent (RWF)-tree and a sorted downward closure property of RWFIs for the RWF-tree are proposed. Moreover, two data structures, named element (E)-table and recent weighted frequent (RWF)-table, are designed to store the information needed for discovering RWFIs. RFWI-Mine discovers RWFIs in a recursive way without candidate generation, thus reducing the computational costs and memory requirements for mining RWFIs. A second improved algorithm named RWFI-EMine algorithm is further proposed to avoid building E-tables and RWF-tables for unpromising itemsets and their child nodes by adopting the Estimated Weight of 2-itemset Pruning (EW2P) strategy. Extensive experiments are conducted on several real-world and synthetic datasets to evaluate the performance of the two proposed algorithms, and the ratio between the number of generated RWFIs and WFIs. Results show that the proposed algorithms outperform not only the traditional PWA algorithm for WFIM, but also the state-of-the-art RWFIM-P and RWFIM-PE algorithms for RWFIM, in terms of runtime, memory usage and scalability.

슬라이딩 윈도우 기반의 스트림 하이 유틸리티 패턴 마이닝 기법 성능분석

최근 무선 센서 네트워크, 사물 인터넷, 소셜 네트워크 서비스와 같은 다양한 응용 분야에서 대용량 스트림 데이터가 실시간으로 생성되고 있으며, 효율적인 기법을 통해 처리 및 분석하여 유용한 정보를 찾아내고, 이를 의사 결정을 위해 사용할 수 있도록 하는 것은 중요한 이슈 중에 하나이다. 스트림 데이터는 끊임없이 빠른 속도로 생성되므로 최소한의 접근을 통해 처리해야 하며, 신속한 저전력 처리를 필요로 하는 자원이 제한된 환경에서 분석될 수 있도록 적합한 기법이 요구된다. 이러한 문제를 해결하기 위해, 슬라이딩 윈도우 개념이 제안되어 연구되고 있다. 한편, 대용량 데이터로부터 의미 있는 정보를 찾아내기 위한 데이터 마이닝 기법 중에 하나인 패턴 마이닝은 중요 정보를 패턴 형태로 추출한다. 전통적인 빈발 패턴 마이닝은 이진 데이터베이스를 대상으로 하고 모든 아이템을 동일한 중요도로 고려함으로써 데이터 마이닝 분야에서 중요한 역할을 수행해 왔지만, 실제 데이터 특성을 반영하지 못하는 단점을 지닌다. 하이 유틸리티 패턴 마이닝은 비 이진 데이터베이스로부터 상대적인 아이템 중요도를 반영하여 더욱 의미 있는 정보를 찾아내기 위해 제안되었다. 정적 데이터를 대상으로 하는 하이 유틸리티 패턴 마이닝 기법은 그러나 스트림 데이터 처리에 적합하지 못하다. 제한된 환경에서 스트림 데이터의 특성을 반영하고 효율적으로 처리하여 중요한 정보를 찾아내기 위해 슬라이딩 윈도우 기반의 접근법이 제안되었다. 본 논문은 슬라이딩 윈도우 기반 하이 유틸리티 패턴 마이닝 기법들의 성능을 평가하고 분석하여 해당 기법들의 특성 및 발전 방향을 고찰한다. Recently, huge stream data have been generated in real time from various applications such as wireless sensor networks, Internet of Things services, and social network services. For this reason, to develop an efficient method have become one of significant issues in order to discover useful information from such data by processing and analyzing them and employing the information for better decision making. Since stream data are generated continuously and rapidly, there is a need to deal with them through the minimum access. In addition, an appropriate method is required to analyze stream data in resource limited environments where fast processing with low power consumption is necessary. To address this issue, the sliding window model has been proposed and researched. Meanwhile, one of data mining techniques for finding meaningful information from huge data, pattern mining extracts such information in pattern forms. Frequency-based traditional pattern mining can process only binary databases and treats items in the databases with the same importance. As a result, frequent pattern mining has a disadvantage that cannot reflect characteristics of real databases although it has played an essential role in the data mining field. From this aspect, high utility pattern mining has suggested for discovering more meaningful information from non-binary databases with the consideration of the characteristics and relative importance of items. General high utility pattern mining methods for static databases, however, are not suitable for handling stream data. To address this issue, sliding window based high utility pattern mining has been proposed for finding significant information from stream data in resource limited environments by considering their characteristics and processing them efficiently. In this paper, we conduct various experiments with datasets for performance evaluation of sliding window based high utility pattern mining algorithms and analyze experimental results, through which we study their characteristics and direction of improvement.

High utility pattern mining over data streams with sliding window technique

Processing changeable data streams in real time is one of the most important issues in the data mining field due to its broad applications such as retail market analysis, wireless sensor networks, and stock market prediction. In addition, it is an interesting and challenging problem to deal with the stream data since not only the data have unbounded, continuous, and high speed characteristics but also their environments have limited resources. High utility pattern mining, meanwhile, is one of the essential research topics in pattern mining to overcome major drawbacks of the traditional framework for frequent pattern mining that takes only binary databases and identical item importance into consideration. This approach conducts mining processes by reflecting characteristics of real world databases, non-binary quantities and relative importance of items. Although relevant algorithms were proposed for finding high utility patterns in stream environments, they suffer from a level-wise candidate generation-and-test and a large number of candidates by their overestimation techniques. As a result, they consume a huge amount of execution time, which is a significant performance issue since a rapid process is necessary in stream data analysis. In this paper, we propose an algorithm for mining high utility patterns from resource-limited environments through efficient processing of data streams in order to solve the problems of the overestimation-based methods. To improve mining performance with fewer candidates and search space than the previous ones, we develop two techniques for reducing overestimated utilities. Moreover, we suggest a tree-based data structure to maintain information of stream data and high utility patterns. The proposed tree is restructured by our updating method with decreased overestimation utilities to keep up-to-date stream information whenever the current window slides. Our approach also has an important effect on expert and intelligent systems in that it can provide users with more meaningful information than traditional analysis methods by reflecting the characteristics of real world non-binary databases in stream environments and emphasizing on recent data. Comprehensive experimental results show that our algorithm outperforms the existing sliding window-based one in terms of runtime efficiency and scalability.

상위 K 하이 유틸리티 패턴 마이닝 기법 성능분석

전통적인 빈발 패턴 마이닝은 데이터베이스로부터 사용자 정의 최소 임계치 이상의 빈도수를 가지는 유효 패턴들을 식별한다. 적절한 임계치 설정은 해당 도메인에 대한 사전 지식을 요구하므로 쉬운 작업이 아니다. 따라서 임계치 설정을 통한 마이닝 결과의 정밀한 제어 불가능으로 인해 도메인 지식을 기반으로 하지 않는 패턴 마이닝 방법이 필요하게 되었다. 상위 K 빈발 패턴 마이닝은 이러한 문제를 해결하기 위해 제안되었으며, 임계치 설정 없이 상위 K개의 중요 패턴들을 마이닝 한다. 사용자는 이를 적용함으로써 데이터베이스에 상관없이 가장 높은 빈도수의 패턴부터 K번째로 높은 빈도수의 패턴까지 찾아낼 수 있다. 비록 상위 K 빈발 패턴 마이닝이 임계치 설정 없이 상위 K개의 중요 패턴들을 마이닝 하지만, 트랜잭션 내 아이템 수량과 데이터베이스 내 서로 다른 아이템 중요도를 고려하지 못하여 많은 실세계 응용의 요구에 부합하지 못한다. 하이 유틸리티 패턴 마이닝은 아이템 중요도가 포함된 비 바이너리 데이터베이스의 특성을 고려하기 위해 제안되었으나 최소 임계치를 필요로 한다. 최근 임계치 설정 없는 하이 유틸리티 패턴 마이닝을 위한 상위 K 하이 유틸리티 패턴 마이닝이 개발되었으며, 이를 통해 사용자는 사전 지식 없이 원하는 수의 패턴을 마이닝 할 수 있다. 본 논문은 상위 K 하이 유틸리티 패턴 마이닝을 위한 알고리즘을 분석한다. 최신 알고리즘에 대한 성능분석을 통해 개선사항 및 발전 방향에 대해 고찰한다. Traditional frequent pattern mining discovers valid patterns with no smaller frequency than a user-defined minimum threshold from databases. In this framework, an enormous number of patterns may be extracted by a too low threshold, which makes result analysis difficult, and a too high one may generate no valid pattern. Setting an appropriate threshold is not an easy task since it requires the prior knowledge for its domain. Therefore, a pattern mining approach that is not based on the domain knowledge became needed due to inability of the framework to predict and control mining results precisely according to the given threshold. Top-k frequent pattern mining was proposed to solve the problem, and it mines top-k important patterns without any threshold setting. Through this method, users can find patterns from ones with the highest frequency to ones with the k-th highest frequency regardless of databases. In this paper, we provide knowledge both on frequent and top-k pattern mining. Although top-k frequent pattern mining extracts top-k significant patterns without the setting, it cannot consider both item quantities in transactions and relative importance of items in databases, and this is why the method cannot meet requirements of many real-world applications. That is, patterns with low frequency can be meaningful, and vice versa, in the applications. High utility pattern mining was proposed to reflect the characteristics of non-binary databases and requires a minimum threshold. Recently, top-k high utility pattern mining has been developed, through which users can mine the desired number of high utility patterns without the prior knowledge. In this paper, we analyze two algorithms related to top-k high utility pattern mining in detail. We also conduct various experiments for the algorithms on real datasets and study improvement point and development direction of top-k high utility pattern mining through performance analysis with respect to the experimental results.

Critical design characteristics for online retail stores in India

Subject area E-commerce. Study level/applicability The case study is specific to the marketing demographics of online Indian shoppers and therefore, the inter-relationship between certain customer requirements and design elements and the relative importance of items in the latter may not follow the same pattern elsewhere. Case overview At a time when e-commerce is booming in India and when online retailers are posting multifold year-on-year growth, it becomes increasingly important to identify the factors pertaining to online stores which can influence the buying behavior of consumers. This case aims to explore such factors relevant to businesses as well as consumers so as to enable the next generation of leaders in online retail business to gain maximally. It deals with critical design characteristics of online retail stores in India which can prove crucial to their success. These characteristics are manifestations of various customer requirements. Two surveys are conducted to establish a hierarchy of design elements and quantify the inter-relationships between customer requirements and design characteristics. This is followed by leads as to which factors may or may not have contributed toward the declining sales volume of an e-commerce start-up, namely, E-Bazaar. Expected learning outcomes The learning objectives of the case include: the study of design characteristics with respect to their relative importance; the analysis of the degree of relationships between the design characteristics and customer requirements; and the interpretation of real-life signs in taking strategic business decisions in the field of e-commerce. The case aims to prepare a new breed of leaders in the e-commerce sector with a good level of relevant business acumen to help them make informed strategic choices. Supplementary materials Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

Decisional Balance: Alcohol Decisional Balance Intervention for Heavy Drinking Undergraduates

Background: This study evaluated a decisional balance intervention among heavy drinking undergraduates and compared a nonweighted decisional balance proportion (DBP; Collins, Carey, & Otto, 2009) to a participant-weighted DBP with weights based on relative importance of items. We expected: (1) the intervention to decrease drinking compared to control; (2) the weighted intervention to be more effective compared to the nonweighted or control in reducing drinking; and (3) intervention efficacy to be moderated by initial DBP. Method: Participants (N = 162, Mean age = 24.37, SD = 6.81, 27% male) were randomly assigned to an alcohol intervention wherein they were either asked to assign weights of importance to pros and cons (weighted intervention), or not (nonweighted intervention), or to control. Participants completed web-based questionnaires at baseline and again during a one month follow-up assessment. Results: Consistent with expectations, the nonweighted intervention was associated with reduced follow-up weekly drinking, and the weighted intervention was associated with reductions in drinking frequency. Results further indicated that initial decisional balance did not moderate intervention efficacy. Discussion: Findings suggest that the decisional balance procedure can reduce drinking but there was not compelling evidence for the addition of weights. This study lays the groundwork for enhancing future interventions by increasing empirical knowledge of the role motivation plays in heavy alcohol use.

Top-k high utility pattern mining with effective threshold raising strategies

In pattern mining, users generally set a minimum threshold to find useful patterns from databases. As a result, patterns with higher values than the user-given threshold are discovered. However, it is hard for the users to determine an appropriate minimum threshold. The reason for this is that they cannot predict the exact number of patterns mined by the threshold and control the mining result precisely, which can lead to performance degradation. To address this issue, top-k mining has been proposed for discovering patterns from ones with the highest value to ones with the kth highest value with setting the desired number of patterns, k. Top-k utility mining has emerged to consider characteristics of real-world databases such as relative importance of items and item quantities with the advantages of top-k mining. Although a relevant algorithm has been suggested in recent years, it generates a huge number of candidate patterns, which results in an enormous amount of execution time. In this paper, we propose an efficient algorithm for mining top-k high utility patterns with highly decreased candidates. For this purpose, we develop three strategies that can reduce the search space by raising a minimum threshold effectively in the construction of a global tree, where they utilize exact and pre-evaluated utilities of itemsets. Moreover, we suggest a strategy to identify actual top-k high utility patterns from candidates with the exact and pre-calculated utilities. Comprehensive experimental results on both real and synthetic datasets show that our algorithm with the strategies outperforms state-of-the-art methods.

A paired comparison approach for the analysis of sets of Likert-scale responses

This paper provides an alternative methodology for the analysis of a set of Likert responses measured on a common attitudinal scale when the primary focus of interest is on the relative importance of items in the set. The method makes fewer assumptions about the distribution of the responses than the more usual approaches such as comparisons of means, MANOVA or ordinal data methods. The approach transforms the Likert responses into paired comparison responses between the items. The complete multivariate pattern of responses thus produced can be analyzed by an appropriately reformulated paired comparison model. The dependency structure between item responses can also be modelled flexibly. The advantage of this approach is that sets of Likert responses can be analyzed simultaneously within the Generalized Linear Model framework, providing standard likelihood-based inference for model selection. This method is applied to a recent international survey on the importance of environmental problems.

A perspective view of dieting to lower the blood cholesterol

As a guide to physicians, patients, and potential patients concerned with the prospect and practicalities of changing the diet to lower the blood cholesterol in the hope of preventing disease, estimates have been made of the importance of various items of food which figure prominently in relation to this topic. The measure of importance adopted is the predicted average servings of these foods, the prediction being based on the content of fatty acids and cholesterol. The major contributors, each adding more than 10 mg/100 ml to the plasma level, include brains, double servings of meat, one egg per day, and butter. At the other extreme, polyunsaturated oils and margarines actively lower plasma cholesterol while oysters, skim milk, and nuts have practically no effect. The relative importance of items, includeing the newer polyunsaturated ruminant meats and other products, has been charted for easy reference. The culinary costs involved in a cholesterol-lowering diet, considered not in monetary terms but as debits and credits in the food statement, must be balances against the potential benefits to be gained from disease prevention.

The Relative Importance of Items of Chemical Information for General Education

The Relative Importance of Items of Chemical Information for General Education