Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Logistics”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 865 records · Page 48Linked to original sources
PubMed1998

Application of logistic growth model to pharmacodynamic analysis of in vitro bactericidal kinetics.

A new pharmacodynamic model for the analysis of in vitro bactericidal kinetics was developed based on the logistic growth model, with the bacterial phases divided into two compartments. The model equations are expressed as nonlinear simultaneous differential equations, and the Runge-Kutta-Gill method was adopted to numerically solve the equations in both the simulation and the least squares curve-fitting procedures. The model can describe the initial killing and the regrowth phases and can explain the nonlinear dependence of the killing rate on the drug concentration. The model can also explain the plateau in the bacterial growth curve that is often observed in in vitro experiments. The model was applied to analysis of the in vitro time-killing data of beta-lactam antibiotics, S-4661, meropenem, imipenem, cefpirome, and ceftazidim against three types of bacteria, Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus. The results of curve-fitting using the least squares program MULTI (Runge) showed good fits for all types of drugs and bacteria. The relationship between the characteristics of the drug-bacteria interactions and the estimated pharmacodynamic parameters is discussed.

Anti-Bacterial Agents↗
PubMed1998

Analysis of survival data with multiple causes of failure: a comparison of hazard- and logistic-regression models with application in demography.

"The purpose of the paper is to compare results of estimation and inference concerning covariate effects as obtained from two approaches to the analysis of survival data with multiple causes of failure. The first approach involves a dynamic model for the cause-specific hazard rate. The second is based on a static logistic regression model for the conditional probability of having had an event of interest. The influence of sociodemographic characteristics on the rate of family initiation and, more importantly, on the choice between marriage and cohabitation as a first union, is examined. We found that results, generally, are similar across the methods considered. Some issues in relation to censoring mechanisms and independence among causes of failure are discussed."

Family Characteristics↗
PubMed1997

Logistics of conducting a large number of individual sessions with a full-scale patient simulator at a scientific meeting.

OBJECTIVE: To design and implement the logistics of accommodating a large number of participants in individual, hands-on sessions on a full-scale patient simulator during a major scientific meeting or continuing medical education course. METHODS: We used our method during the 11th World Congress of Anaesthesiologists in Sydney, Australia to facilitate studying the impact of pulse oximetry and capnography on the time taken by anesthesiologists to correctly identify critical incidents on a full-scale patient simulator. Each study participant spent 15 minutes in 4 sections of the study area: the anesthesia and monitoring equipment briefing room, the simulator briefing room, the simulation room and the debriefing room. RESULTS: There were 113 participants during five days (15 during instructor training and 25, 23, 24 and 26 on subsequent exhibit days). We were oversubscribed daily. However, there were 9 no-shows during the 4 days of the study, which generated a participant absence rate of 9.2%. The average number of participants over the 4 days of the study was 24.5 per day compared to our capacity of 27 per day. The feedback we obtained from the participants about the simulation experience and the format of the exercise was positive and enthusiastic. CONCLUSIONS: We have developed a practical and viable method that can be adapted for use at scientific meetings and courses, which improves accessibility of individual, hands-on sessions on full-scale patient simulators to a larger audience than previously attainable. Our method is applicable for continuing medical education courses as well as research purposes in the form of prospective studies during scientific meetings and courses.

Anesthesiology↗
PubMed2002

Digital dermoscopy analysis of atypical pigmented skin lesions: a stepwise logistic discriminant analysis approach.

BACKGROUND: Digital microscopy is a non-invasive diagnostic technique enabling determination of characteristics that cannot be appreciated by direct observation. If correctly applied, this technique can be useful for the diagnosis of pigmented skin lesions. PURPOSE: To evaluate the utility of digital microscopy for analysing atypical benign and malignant pigmented skin lesions exploiting digital numerical filtering and automatic measurements. METHODS: Forty-eight parameters were identified as possible discriminating variables, and were grouped in four categories: geometries, colours, textures, and islands of colour. Statistical analysis was used to identify the variables with the highest discriminating power. RESULTS: The high quality of the digital image made it possible to observe diagnostic signs in pigmented skin lesion images, acquired by the present technique, in great detail. Specially designed filtering enhanced certain diagnostic patterns. Stepwise discriminant analysis selected only 10 variables (the means of these variables were higher in melanomas than in nevi). CONCLUSIONS: The combined use of digital dermoscopy and stepwise logistic discriminant analysis made it possible to single out the best objective variables for distinguishing atypical nevi and early melanoma.

Diagnosis, Computer-Assisted↗
PubMed2004

Factors affecting the probability of first year medical student dropout in the UK: a logistic analysis for the intake cohorts of 1980-92.

BACKGROUND: In the context of the 1997 Report of the Medical Workforce Standing Advisory Committee, it is important that we develop an understanding of the factors influencing medical school retention rates. AIMS: To analyse the determinants of the probability that an individual medical student will drop out of medical school during their first year of study. METHOD: Binomial and multinomial logistic regression analysis of individual-level administrative data on 51 810 students in 21 medical schools in the UK for the intake cohorts of 1980-92 was performed. RESULTS: The overall average first year dropout rate over the period 1980-92 was calculated to be 3.8%. We found that the probability that a student would drop out of medical school during their first year of study was influenced significantly by both the subjects studied at A-level and by the scores achieved. For example, achieving 1 grade higher in biology, chemistry or physics reduced the dropout probability by 0.38% points, equivalent to a fall of 10%. We also found that males were about 8% more likely to drop out than females. The medical school attended also had a significant effect on the estimated dropout probability. Indicators of both the social class and the previous school background of the student were largely insignificant. CONCLUSIONS: Policies aimed at increasing the size of the medical student intake in the UK and of widening access to students from non-traditional backgrounds should be informed by evidence that student dropout probabilities are sensitive to measures of A-level attainment, such as subject studied and scores achieved. If traditional entry requirements or standards are relaxed, then this is likely to have detrimental effects on medical schools' retention rates unless accompanied by appropriate measures such as focussed student support.

Adult↗
PubMed1999

Community participation in the recruitment and retention of rural doctors: methodological and logistical considerations.

Two Queensland rural communities with histories of poor general practitioner (GP) recruitment and retention participated in a process aimed at developing broadly based community action plans to recruit and retain GPs. Despite their very different physical and social characteristics, the two communities developed many similar objectives and strategies, which had the possibility of being implemented more widely. The community participation process can be both time- and cost-effective if consideration is given to a variety of methodological and logistical issues. The process is a means by which communities, Divisions of General Practice, government, academic institutions and others can work together to recruit and retain medical practitioners.

Community Participation↗
PubMed2002

Association between blood pressure, ultrafiltration, and hemodialysis graft thrombosis: a multivariable logistic regression analysis.

Although a low blood flow (Q(a)) is the most important cause of graft thrombosis, several studies have shown that Q(a) measurements do not accurately predict thrombosis. This suggests that additional variables may influence thrombosis. Identification of such variables may be essential to designing surveillance protocols that accurately predict thrombosis. In this nested case-control study, we prospectively followed 105 patients for up to 2.5 years in order to test the association of a number of variables with thrombosis. These included Q(a) (monthly by ultrasound dilution), percentage stenosis (quarterly by duplex ultrasound), mean arterial pressure (MAP), percentage ultrafiltration (%UF) during dialysis (%UF = 100[liters]/[kilogram of weight]), and other variables that defined patient and graft characteristics. Patients were divided into patent (n = 53) and thrombosed groups (n = 52), and MAP and %UF from seven consecutive dialysis sessions were analyzed. In the thrombosed group, the last session was the final session before thrombosis. A multivariable logistic regression model showed that Q(a), MAP (the predialysis average of seven sessions), and %UF (from the last session) were independently associated with thrombosis, whereas all other variables were not. The model yielded the following odds ratios for thrombosis: for a single Q(a) value (reduction of 1,000 mL/min), 12.0 (P < 0.01); for %UF (increase of 4%), 5.3 (P < 0.01); for MAP (reduction of 30 mm Hg), 4.1 (P = 0.02); and for percentage decrease in Q(a) (> or =20% versus <20%), 2.4 (P = 0.12). We conclude that in addition to Q(a), both %UF at the last session before thrombosis and average predialysis MAP from seven sessions are independently associated with thrombosis. These results help explain why Q(a) alone does not accurately predict thrombosis. A prospective study is needed to determine whether %UF at each session and a moving average MAP from seven sessions improve the prediction of thrombosis. However, it should be recognized that a large %UF is a preterminal event that likely provides too short a warning for intervention before thrombosis.

Blood Pressure↗
PubMed2001

[Optimized logistics in the prehospital management of acute stroke].

OBJECTIVE: Current management of acute stroke is characterised by an aggressive approach including specific therapy i. e. reperfusion therapy. However currently stroke patients often arrive too late in hospitals offering adequate treatment. Therefore optimized logistics play a predominant role in modern stroke management. AIMS OF THE STUDY: 1. Does teaching of EMS staff and the public result in reduced prehospital latencies 2. Will EMS personnel be able to effectively screen patients potentially suitable for thrombolysis? METHODS: During a six week-period all EMS patients presenting with possible signs of an acute stroke were prospectively registered (period 1). Data of interest were age, mode of primary contact, prehospital latencies, mode of transportation, destination and final diagnosis. Next an algorithm was established allowing EMS personnel to transfer patients with an assumed stroke to the best suitable hospital. Teaching comprised clinical signs, indication of CT scanning, pathophysiology, specific therapeutic options (thrombolysis), and criteria to identify patients suitable for thrombolysis. In a second step the public was continuously taught about stroke symptoms and the necessity to instantly seek EMS assistance. After 12 months data were compared to baseline (period 2). RESULTS: (period 2 vs. Period 1): Rate of patients transferred to a stroke center: 60 % vs. 54 %; rate of those transported to hospitals not offering CT scans: 17 % vs. 26 % (p < 0.05). Percentage of patients primarily contacting the EMS system: 33 % vs. 24 %. Median interval between onset of symptoms and emergency call: 54 vs. 263 minutes Median interval between the emergency call and arrival at the emergency department: 44 vs. 58 minutes (p < 0.01). Rate of patients admitted with a diagnosis other than stroke: 18 % vs. 25 % (n. s.). Median interval between onset of symptoms and hospital admission: 140 vs. 368 minutes (p < 0.001). Median age: 69 vs. 75 years (p < 0.01). CONCLUSION: This study demonstrates the efficacy of educational efforts in reducing latencies and in screening patients potentially suitable for thrombolysis. Future efforts will comprise more intense education of a high risk subpopulation.

Age Factors↗
PubMed1981

Lesion distribution, multiple infection, and the logistic increase of plant disease.

The lesions of few of the 112 observed cases of plant disease were randomly distributed; most were fitted by the negative binomial function with exponent approximately 1. The proportion of healthy hosts generally equaled the frequency of zero in the negative binomial. The same course of an epidemic is calculated for the logistic increase of infected plants or for plants infected by propagules produced in proportion to lesions and distributed with exponent equal 1.

Journal Article↗
PubMed1996

Two-stage designs for the logistic regression model in single-agent bioassays.

In this paper we focus on the use of a two-stage procedure for logistic regression that emphasizes predicting response through the use of the Q-optimality criterion. The use of D-optimality in the first stage is primarily to allow best possible parameter estimates as one enters the second stage. However, it is important to understand that there are many ways to formulate the two-stage procedure. It may involve any optimality criterion in either stage. In fact, theoretically, one need not stop at two stages. It was our intention in this paper to demonstrate the potential in the two-stage procedure in cases in which good initial parameter estimates are not available. Those investigators who are interested in the software for the two-stage procedure described here should contact Dr. William R. Myers.

Animals↗
PubMed2000

Calibration and LOD/LOQ estimation of a chemiluminescent hybridization assay for residual DNA in recombinant protein drugs expressed in E. coli using a four-parameter logistic model.

Calibration is the process of fitting a model based on reference data points (x, y), then using the model to estimate an unknown x based on a new measured response, y. In DNA assay, x is the concentration, and y is the measured signal volume. A four-parameter logistic model was used frequently for calibration of immunoassay when the response is optical density for enzyme-linked immunosorbent assay (ELISA) or adjusted radioactivity count for radioimmunoassay (RIA). Here, it is shown that the same model or a linearized version of the curve are equally useful for the calibration of a chemiluminescent hybridization assay for residual DNA in recombinant protein drugs and calculation of performance measures of the assay.

Calibration↗
PubMed2006

Diffusion kernel-based logistic regression models for protein function prediction.

Assigning functions to unknown proteins is one of the most important problems in proteomics. Several approaches have used protein-protein interaction data to predict protein functions. We previously developed a Markov random field (MRF) based method to infer a protein's functions using protein-protein interaction data and the functional annotations of its protein interaction partners. In the original model, only direct interactions were considered and each function was considered separately. In this study, we develop a new model which extends direct interactions to all neighboring proteins, and one function to multiple functions. The goal is to understand a protein's function based on information on all the neighboring proteins in the interaction network. We first developed a novel kernel logistic regression (KLR) method based on diffusion kernels for protein interaction networks. The diffusion kernels provide means to incorporate all neighbors of proteins in the network. Second, we identified a set of functions that are highly correlated with the function of interest, referred to as the correlated functions, using the chi-square test. Third, the correlated functions were incorporated into our new KLR model. Fourth, we extended our model by incorporating multiple biological data sources such as protein domains, protein complexes, and gene expressions by converting them into networks. We showed that the KLR approach of incorporating all protein neighbors significantly improved the accuracy of protein function predictions over the MRF model. The incorporation of multiple data sets also improved prediction accuracy. The prediction accuracy is comparable to another protein function classifier based on the support vector machine (SVM), using a diffusion kernel. The advantages of the KLR model include its simplicity as well as its ability to explore the contribution of neighbors to the functions of proteins of interest.

Databases, Protein↗
PubMed1988

Cytologic criteria for fibroadenoma. A step-wise logistic regression analysis.

The authors reviewed the fine-needle aspiration biopsy smears from 62 subjects with proven fibroadenoma, 60 subjects with proven ductal carcinoma, and 42 subjects with proven fibrocystic disease. All smears were coded as to the presence or absence of the following variables: epithelial cells, stroma, honeycomb sheets, antler horn clusters, naked nuclei, nucleoli, marked cellularity, foam cells, apocrine cells, anisokaryosis, atypical nuclear hyperchromasia, single cells with cytoplasm, mitotic figures, and nuclei greater than two red blood cell diameters. Step-wise logistic regression analyses were performed to determine the variables predictive of fibroadenoma. The statistical analyses selected stroma, antler horn clusters, and marked cellularity as the key cytologic criteria to differentiate fibroadenomas from fibrocystic disease. The statistical analyses selected stroma, antler horn clusters, and honeycomb sheets as the key cytologic criteria to differentiate fibroadenoma from ductal carcinoma.

Adenofibroma↗
PubMed2003

A simple and efficient algorithm for gene selection using sparse logistic regression.

MOTIVATION: This paper gives a new and efficient algorithm for the sparse logistic regression problem. The proposed algorithm is based on the Gauss-Seidel method and is asymptotically convergent. It is simple and extremely easy to implement; it neither uses any sophisticated mathematical programming software nor needs any matrix operations. It can be applied to a variety of real-world problems like identifying marker genes and building a classifier in the context of cancer diagnosis using microarray data. RESULTS: The gene selection method suggested in this paper is demonstrated on two real-world data sets and the results were found to be consistent with the literature. AVAILABILITY: The implementation of this algorithm is available at the site http://guppy.mpe.nus.edu.sg/~mpessk/SparseLOGREG.shtml SUPPLEMENTARY INFORMATION: Supplementary material is available at the site http://guppy.mpe.nus.edu.sg/~mpessk/SparseLOGREG.shtml

Algorithms↗
PubMed2004

Classification using partial least squares with penalized logistic regression.

MOTIVATION: One important aspect of data-mining of microarray data is to discover the molecular variation among cancers. In microarray studies, the number n of samples is relatively small compared to the number p of genes per sample (usually in thousands). It is known that standard statistical methods in classification are efficient (i.e. in the present case, yield successful classifiers) particularly when n is (far) larger than p. This naturally calls for the use of a dimension reduction procedure together with the classification one. RESULTS: In this paper, the question of classification in such a high-dimensional setting is addressed. We view the classification problem as a regression one with few observations and many predictor variables. We propose a new method combining partial least squares (PLS) and Ridge penalized logistic regression. We review the existing methods based on PLS and/or penalized likelihood techniques, outline their interest in some cases and theoretically explain their sometimes poor behavior. Our procedure is compared with these other classifiers. The predictive performance of the resulting classification rule is illustrated on three data sets: Leukemia, Colon and Prostate.

Algorithms↗
PubMed1993

Logistic regression analysis of fixed patient factors for postoperative sickness: a model for risk assessment.

One hundred and forty-seven patients undergoing minor orthopaedic surgery were studied prospectively by logistic regression analysis to determine the association of independent fixed patient factors with the incidence of postoperative sickness (nausea, retching or vomiting). Gender, history of previous postoperative sickness, postoperative opioids and interaction between gender and previous history of sickness were significant independent factors for postoperative sickness; history of motion sickness was weakly associated. The probability of postoperative sickness in the first 24 h after surgery may be estimated from the equation: logit postoperative sickness = -5.03+2.24(postoperative opioids) +3.97 (previous sickness history) +2.4 (gender) +0.78 (motion sickness) -3.2 (gender x previous sickness history). (Log likelihood ratio test for 5 degrees of freedom for the coefficients, chi-square = 53.5 (P < 0.001).) It is suggested that the calculated probability for sickness may be a useful addition for balancing patient treatment groups and allowing between-study comparisons.

Adolescent↗
PubMed1998

Hospital mortality after urgent and emergency laparotomy in patients aged 65 yr and over. Risk and prediction of risk using multiple logistic regression analysis.

We studied 107 patients aged over 65 years undergoing urgent or emergency laparotomy. Aspects of preoperative assessment, perioperative management and postoperative care were analysed by multiple logistic regression to determine the factors that predicted hospital survival. We determined which factors influenced anaesthetists' prediction that patients would survive. These predictions were made both before and immediately after operation. The factors associated with the use of invasive cardiovascular monitoring were also studied. We obtained a model that accounted for 93% of the variability in the likelihood of survival. Age and ASA status were significant predictors of survival (P < 0.05), and of anaesthetists' prediction of mortality both before and after operation. Several other factors were significant determinants of survival but were not determinants of the anaesthetist's opinion regarding survival.

Aged↗
PubMed2006

Logistic support service improves processes and outcomes of diabetes care in general practice.

BACKGROUND: Guidelines for type 2 diabetes care in general practice are well known and accepted, but the implementation falls short. OBJECTIVE: To implement these guidelines by introducing a diabetes support service (DSS) to support the care delivered by the GP. METHODS: A controlled, non-randomised study with delayed intervention in the control group; 78 GPs (n=51 for the intervention and n=21 for the control group) in the south of the Netherlands and 613 of their type 2 diabetic patients participated. Data were collected on the frequency, content and results of the check-ups (fasting blood glucose, HbA1c, cholesterol, cholesterol/HDL ratio, triglycerides, creatinine, blood pressure, fundus photography, foot examination, body mass index and smoking status) for 3 years. The year before signing up with the DSS was taken for the pre-measurements and after 2 years of DSS the post-measurements took place. The effect of the DSS was analysed in a mixed model with repeated measurement covariance structure. RESULTS: At baseline the intervention and control group did not differ in control frequency and outcome (HbA1c). After the intervention the percentage of patients that attended four or more quarterly check-ups (with at least testing of fasting blood glucose or HbA1c) increased from 59 to 78%. In contrast, the frequency of check-ups in the control group remained constant. This effect was significant. The HbA1c remained the same in the intervention group while there was a significant deterioration in the HbA1c in the control group. CONCLUSION: Simple logistic support by the DSS proved to have the capacity to implement type 2 diabetes guidelines in general practice.

Aged↗