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 703 records · Page 39Linked to original sources
PubMed2005

The severity of Minamata disease declined in 25 years: temporal profile of the neurological findings analyzed by multiple logistic regression model.

Minamata disease (MD) was caused by ingestion of seafood from the methylmercury-contaminated areas. Although 50 years have passed since the discovery of MD, there have been only a few studies on the temporal profile of neurological findings in certified MD patients. Thus, we evaluated changes in neurological symptoms and signs of MD using discriminants by multiple logistic regression analysis. The severity of predictive index declined in 25 years in most of the patients. Only a few patients showed aggravation of neurological findings, which was due to complications such as spino-cerebellar degeneration. Patients with chronic MD aged over 45 years had several concomitant diseases so that their clinical pictures were complicated. It was difficult to differentiate chronic MD using statistically established discriminants based on sensory disturbance alone. In conclusion, the severity of MD declined in 25 years along with the modification by age-related concomitant disorders.

Adolescent↗
PubMed1996

A logistic-regression model provides novel guidelines to maximize the anti-acute rejection properties of cyclosporine with a minimum of toxicity.

Although cyclosporine has become the mainstay of immunosuppression in organ transplantation, there is still no consensus on the criteria to optimize its anti-rejection activity with minimum toxicity. A clear and objective definition of target cyclosporine trough levels at different times from renal transplantation is still lacking, primarily because of the lack of a model correlating cyclosporine levels with probability of rejection or toxicity. In this study, logistic-regression model was developed that was applied to data collected retrospectively from two postoperative periods, i.e., Days 0 to 9 and 10 to 30, in 135 consecutive cadaveric renal transplant recipients, for a total of 1851 determinations. Only minimum and maximum trough levels were considered for each period. Concentration-response curves were estimated for Days 0 to 9 (P = 0.0001 for efficacy and P = 0.028 for toxicity) and for Days 10 to 30 (P = 0.015 for efficacy and P = 0.037 for toxicity). Therapeutic intervals of 330 to 430 ng/mL (parent compound in whole blood) for Days 0 to 9 and 260 to 390 ng/mL for Days 10 to 30 predicted an incidence of acute rejection of 22% and 12%, respectively, with a reasonably low toxicity that primarily consisted of elevation of serum aminotransferases.

Adult↗
PubMed2005

An evaluation of logistic regression models for predicting amphipod toxicity from sediment chemistry.

An empirical screening level approach was developed to assess the probability of toxicity to benthic organisms associated with contaminated sediment exposure. The study was based on simple logistic regression models (LRMs) of matching sediment chemistry and toxicity data retrieved from a large database of field-collected sediment samples contaminated with multiple chemicals. Three decisions were made to simplify the application of LRMs to sediment samples contaminated with multiple chemicals. First, percent mortality information associated with each sediment sample was condensed into a dichotomous response (i.e., toxic or nontoxic). Second, each LRM assumed that toxicity was attributable to a single contaminant. Third, individual contaminants present at low concentrations were excluded from toxic sediment samples. Based on an analysis of the National Sediment Inventory database, the LRM approach classified 55% of nontoxic sediments as toxic (i.e., false-positives). Because this approach has been used to assess the probability of benthic toxicity as reported by the U.S. Environmental Protection Agency (U.S. EPA), the resultant estimates of potential toxicity convey a misleading impression of the increased hazard that sediments pose to the health of aquatic organisms at many sites in the United States. This could result in important resources needlessly being diverted from truly contaminated sites to evaluate and possibly remediate sediments at uncontaminated sites.

Amphipoda↗
PubMed1995

Prediction of progression of the curve in girls who have adolescent idiopathic scoliosis of moderate severity. Logistic regression analysis based on data from The Brace Study of the Scoliosis Research Society.

In a study conducted by the Scoliosis Research Society, 159 girls with a mean age of thirteen years (range, ten to fifteen years) who had adolescent idiopathic scoliosis were followed prospectively until skeletal maturity or until the curve had increased 6 degrees or more. All patients had had an initial curve of 25 to 35 degrees and an apical level between the eighth thoracic and first lumbar vertebrae, inclusive. Of the 159 patients, 120 were observed without treatment and thirty-nine were managed with lateral electrical surface stimulation. The curve progressed at least 6 degrees in eighty patients. There was no apparent difference in the outcome between the patients who were managed with observation only and those who were given electrical stimulation. Logistic regression analysis was performed to determine which of eleven factors were predictive of progression of the scoliotic curve. A Risser sign of 0 or 1, an apical level cephalad to the twelfth thoracic vertebra, and an imbalance of ten millimeters or less were found to be independently prognostic of progression of more than 6 degrees. A prognostic model that included these three factors and chronological age allowed correct classification of the curve as either progressive or non-progressive in 81 per cent of these patients who had a thoracic or thoracolumbar adolescent idiopathic scoliosis. The positive predictive value was 82 per cent, the negative predictive value was 80 per cent, and the sensitivity and specificity were each 81 per cent.

Adolescent↗
PubMed2000

Clinical evaluation of idiopathic interstitial pneumonia and interstitial pneumonia associated with collagen vascular disease using logistic regression analysis.

OBJECTIVE: To clarify the differences in the clinical features between idiopathic interstitial pneumonia (IIP) and interstitial pneumonia associated with collagen vascular diseases (CVD-IP). METHODS: Symptoms, radiographic findings, pulmonary function, blood chemistry data including autoantibody, and bronchoalveolar lavage fluid (BALF) findings were compared using multiple logistic regression analysis. PATIENTS: The subjects were 44 patients clinically diagnosed with IIP and 33 patients with CVD-IP. RESULTS: The clinical features related to IIP were as follows: male sex, advanced age, past history of hypertension, presence of cough, exertional dyspnea, digital clubbing, an increased level of gamma-globulin, decreased lung volume on chest X-ray, and typical type according to the criteria for IIP on chest X-ray. Increased levels of rheumatoid factor and total cell number in BALF were related to CVD-IP. CONCLUSION: These findings are considered to be useful to differentiate IIP and CVD-IP.

Adult↗
PubMed2002

Logistic model analysis of neurological findings in Minamata disease and the predicting index.

OBJECTIVE: To establish a statistical diagnostic method to identify patients with Minamata disease (MD) considering factors of aging and sex, we analyzed the neurological findings in MD patients, inhabitants in a methylmercury polluted (MP) area, and inhabitants in a non-MP area. MATERIALS AND METHODS: We compared the neurological findings in MD patients and inhabitants aged more than 40 years in the non-MP area. Based on the different frequencies of the neurological signs in the two groups, we devised the following formula to calculate the predicting index for MD: predicting index = 1/(1+e(-x)) x 100 (The value of x was calculated using the regression coefficients of each neurological finding obtained from logistic analysis. The index 100 indicated MD, and 0, non-MD). RESULTS: Using this method, we found that 100% of male and 98% of female patients with MD (95 cases) gave predicting indices higher than 95. Five percent of the aged inhabitants in the MP area (598 inhabitants) and 0.2% of those in the non-MP area (558 inhabitants) gave predicting indices of 50 or higher. CONCLUSION: Our statistical diagnostic method for MD was useful in distinguishing MD patients from healthy elders based on their neurological findings.

Adult↗
PubMed1999

Characterization of breast masses by dynamic enhanced MR imaging. A logistic regression analysis.

PURPOSE: To identify features useful for differentiation between malignant and benign breast neoplasms using multivariate analysis of findings by MR imaging. MATERIAL AND METHODS: In a retrospective analysis, 61 patients with 64 breast masses underwent MR imaging and the time-signal intensity curves for precontrast dynamic postcontrast images were quantitatively analyzed. Statistical analysis was performed using a logistic regression model, which was prospectively tested in another 34 patients with suspected breast masses. RESULTS: Univariate analysis revealed that the reliable indicators for malignancy were first the appearance of the tumor border, followed by the washout ratio, internal architecture after contrast enhancement, and peak time. The factors significantly associated with malignancy were irregular tumor border, followed by washout ratio, internal architecture, and peak time. For differentiation between benignity and malignancy, the maximum cut-off point was to be found between 0.47 and 0.51. In a prospective application of this model, 91% of the lesions were accurately discriminated as benign or malignant lesions. CONCLUSION: Combination of contrast-enhanced dynamic and postcontrast-enhanced MR imaging provided accurate data for the diagnosis of malignant neoplasms of the breast. The model had an accuracy of 91% (sensitivity 90%, specificity 93%).

Adult↗
PubMed2005

Genetic parameters of claw and foot disorders estimated with logistic models.

The primary aim of this study was to estimate heritabilities for different types of claw and foot disorders and the genetic relationship of disorders with milk yield and selected conformation traits by applying logistic models in Holstein dairy cattle. The study included data from 5634 Holstein cows kept on large-scale dairy farms in Eastern Germany. Dichotomous response variables were the presence or absence of the disorder in 2003. Cows that were present in herds for <6 wk in 2003 were excluded from the analysis. Incidences, disregarding repeated measurements, for digital dermatitis (DD), sole ulceration (SU), wall disorder (WD), and interdigital hyperplasia (IH) in rear legs were 13.2, 16.1, 9.6, and 6.3%, respectively. The herd effect was highly significant for all disorders. Incidences increased with increasing parities for SU and WD, but were highest among heifers for DD. High milk yield at the first 2 test d after calving was associated with a greater risk for claw and foot disorders in the same lactation. Estimates of heritability were 0.073 for DD, 0.086 for SU, 0.104 for WD, and 0.115 for IH. Genetically, health problems appear to occur in clusters (i.e., a cow showing one disease has an increased genetic risk of showing another claw disease). This phenomenon was also observed between claw and foot disorders and the somatic cell score. Genetic correlations between milk yield in early lactation and disorders were 0.240 for DD, 0.057 for SU, 0.270 for WD, and 0.336 for IH, indicating a physiological antagonism. Correlations between breeding values for claw and foot disorders of bulls and official breeding values for functional type traits were mostly favorable. Routine recording of claw data will offer a new chance to improve claw health within the population as was elaborated by different scenarios applying selection index procedures.

Animals↗
PubMed2004

Improvement of new logistic model for bacterial growth.

Recently Fujikawa et al. [J. Food Hyg. Soc. Japan, 44, 155-160 (2003)] developed a new logistic model for bacterial growth. In the present study, an adjustment factor in the model was improved. The improved model could successfully describe growth curves of Escherichia coli and Salmonella in liquid media. In particular, the model could describe the linear growth at the early logarithmic phase more accurately than the previous model, being similar in this respect to the Baranyi model. However, the improved model more accurately predicted the rate constant of growth and the duration of the lag time as compared with the Baranyi model. These results showed that the improved model has the potential to successfully predict microbial growth.

Bacteria↗
PubMed1998

Logistic analysis of adaptive gender variants of smoking youths behavior.

The objective of this study was to analyze some specific features of adolescent smokers' self-perception and general attitude to living. All students of a highly qualified secondary school in Stara Zagora were asked to fill in a prepared especially for the study, self-completed questionnaires. Response rate was 79% (n = 174). Logistic analysis was used in order to describe abstract tendency of the direct significant evidences. The data were found that smoking youths had more emphasized emotional and sensual motives and stereotypes. This predisposes them to wrong behavioral decisions and smoking probably is one of the harmful results of such irrational approach.

Adolescent↗
PubMed1998

Using classification tree and logistic regression methods to diagnose myocardial infarction.

Early and accurate diagnosis of myocardial infarction (MI) in patients who present to the Emergency Room (ER) complaining of chest pain is an important problem in emergency medicine. A number of decision aids have been developed to assist with this problem but have not achieved general use. Machine learning techniques, including classification tree and logistic regression (LR) methods, have the potential to create simple but accurate decision aids. Both a classification tree (FT Tree) and an LR model (FT LR) have been developed to predict the probability that a patient with chest pain is having an MI based solely upon data available at time of presentation to the ER. Training data came from a data set collected in Edinburgh, Scotland. Each model was then tested on a separate Edinburgh data set, as well as on a data set from a different hospital in Sheffield, England. Previously published models, the Goldman classification tree[1] and Kennedy LR equation[2], were evaluated on the same test data sets. On the Edinburgh test set, results showed that the FT Tree, FT LR, and Kennedy LR performed equally well, with ROC curve areas of 94.04%, 94.28%, and 94.30%, respectively, while the Goldman Tree's performance was significantly poorer, with an area of 84.03%. The difference in ROC areas between the first three models and the Goldman model is significant beyond the 0.0001 level. On the Sheffield test set, results showed that the FT Tree, FT LR, and Kennedy LR ROC areas were not significantly different (p > = 0.17), while the FT Tree again outperformed the Goldman Tree (p = 0.006). Unlike previous work[3], this study indicates that classification trees, which have certain advantages over LR models, may perform as well as LR models in the diagnosis of patients with MI.

Algorithms↗
PubMed1999

A genetic algorithm to select variables in logistic regression: example in the domain of myocardial infarction.

Actual use of regression models in clinical practice depends on model simplicity. Reducing the number of variables in a model contributes to this goal. The quality of a particular selection of variables for a logistic regression model can be defined in terms of the number of variables selected and the model's discriminatory performance, as measured by the area under the ROC curve. A genetic algorithm was applied to search for the best variable combinations for modeling presence of myocardial infarction in a data set of patients with chest pain. Using an external validation set, the resulting model was compared with models constructed with standard backward, forward and stepwise methods of variable selection. The improvement in discriminatory ability yielded by the genetic algorithm variable selection method was statistically significant (p < 0.02).

Algorithms↗
PubMed1999

[A predictive model for affect of atopic dermatitis in infancy by neural network and multiple logistic regression].

OBJECTS: To analyze the predictive accuracy of the predictive model for affect of atopic dermatitis in infancy, from the data of the epidemiological survey, which were conducted for 10,000 of mothers of infants and children in 1993. SUBJECTS AND METHODS: A total of 4610 replies were received: 2714 from mothers of infants (12 month old) and 1,896 from mothers of children (2 years old). The sensitivity, specificity and predictive accuracy were calculated from probabilistic model by neural network analysis (NNA) and multiple logistic regression analysis (MLA). RESULTS: Risk factors for probabilistic model by NNA were family history (father, mother, siblings, grand father, grand mother), food restriction, food allergy, age, food restriction of mother, egg introduced time, cow's milk introduced time. The sensitivity, specificity and predictive accuracy of NNA model was 88.6%, 99.5% and 96.4%, respectively and MLA model was 75.1%, 82.6% and 82.3%, respectively. CONCLUSION: These results suggest that the NNA is a good and useful method for prediction of onset of AD than MLA. Furthermore, It is necessary to investigate the artificial neural networks for diagnosis and/or treatment by physician.

Child, Preschool↗
PubMed2000

Logistic regression and survival analysis of 450 impotent patients treated with injection therapy: long-term dropout parameters.

PURPOSE: We report the dropout rate associated with intracavernosal self-injection for erectile dysfunction at long-term followup, and determine parameters related to dropout. MATERIALS AND METHODS: Of 2,252 impotent patients evaluated during 9 years 450 (20%) enrolled in our self-injection program. Papaverine and phentolamine were given in 53% (initial treatment), prostaglandin E1 in 21%, and papaverine, phentolamine and prostaglandin E1 (triple mixture) in 26% of cases. Average injection volume for the 3 injection types was 0.52 cc. Data were retrospectively reviewed for patient dropout versus nondropout, specifically for type of drug, volume injected, changing treatment (from 1 drug and/or dosage to another), patient age, marital status, ethnic group, impotence duration and diabetes. RESULTS: Patient age ranged from 23 to 79 years (mean age 55). Of the patients treated at least 4 months 155 (35%) dropped out of the study. Mean treatment duration was 3.5 years. Logistic regression suggested that type of drug, changing treatment during the course of therapy and dosage (volume injected) were the only significant (p <0.05) parameters influencing dropout. Papaverine and phentolamine were twice as likely to lead to dropout as triple mixture or prostaglandin E1, and injection greater than 0.5 cc was more than twice as likely to lead to dropout. Demographic, ethnic, age and etiological factors had no significant effect on the overall dropout rate. CONCLUSIONS: The effectiveness of injection therapy was an underlying factor determining the long-term dropout rate for patients with erectile dysfunction. Other parameters or combinations of parameters that may influence patient dropout remain to be explored.

Erectile Dysfunction↗
PubMed2000

Discrimination between neoplastic and nonneoplastic brain lesions by use of proton MR spectroscopy: the limits of accuracy with a logistic regression model.

BACKGROUND AND PURPOSE: The most accurate method of clinical MR spectroscopy (MRS) interpretation remains an open question. We sought to construct a logistic regression (LR) pattern recognition model for the discrimination of neoplastic from nonneoplastic brain lesions with MR imaging-guided single-voxel proton MRS data. We compared the LR sensitivity, specificity, and receiver operator characteristic (ROC) curve area (Az) with the sensitivity and specificity of blinded and unblinded qualitative MRS interpretations and a choline (Cho)/N-acetylaspartate (NAA) amplitude ratio criterion. METHODS: Consecutive patients with suspected brain neoplasms or recurrent neoplasia referred for MRS were enrolled once final diagnoses were established by histopathologic examination or serial neurologic examinations, laboratory data, and imaging studies. Control spectra from healthy adult volunteers were included. An LR model was constructed with 10 input variables, including seven metabolite resonance amplitudes, unsuppressed brain water content, water line width, and the final diagnosis (neoplasm versus nonneoplasm). The LR model output was the probability of tumor, for which a cutoff value was chosen to obtain comparable sensitivity and specificity. The LR sensitivity and specificity were compared with those of qualitative blinded interpretations from two readers (designated A and B), qualitative unblinded interpretations (in aggregate) from a group of five staff neuroradiologists and a spectroscopist, and a quantitative Cho/NAA amplitude ratio > 1 threshold for tumor. Sensitivities and specificities for each method were compared with McNemar's chi square analysis for binary tests and matched data with a significance level of 5%. ROC analyses were performed where possible, and Az values were compared with Metz's method (CORROC2) with a 5% significance level. RESULTS: Of the 99 cases enrolled, 86 had neoplasms and 13 had nonneoplastic diagnoses. The discrimination of neoplastic from control spectra was trivial with the LR, reflecting high homogeneity among the control spectra. An LR cutoff probability for tumor of 0.8 yielded a specificity of 87%, a comparable sensitivity of 85%, and an area under the ROC curve of 0.96. Sensitivities, specificities, and ROC areas (where available) for the other methods were, on average, 82%, 74%, and 0.82, respectively, for readers A and B, 89% (sensitivity) and 92% (specificity) for the group of unblinded readers, and 79% (sensitivity), 77% (specificity), and 0.84 (Az) for the Cho/NAA > 1 criterion. McNemar's analysis yielded significant differences in sensitivity (n approximately 86 neoplasms) between the LR and reader A, and between the LR and the Cho/NAA > 1 criterion. The differences in specificity between the LR and all other methods were not significant (n approximately 13 nonneoplasms). Metz's analysis revealed a significant difference in Az between the LR and the Cho/NAA ratio criterion.

Adolescent↗
PubMed2000

One model, several results: the paradox of the Hosmer-Lemeshow goodness-of-fit test for the logistic regression model.

BACKGROUND: The Hosmer-Lemeshow test, used extensively to assess the fit of the logistic regression model, is performed by several statistical packages. Recent studies have shown some problems in the use of this test when ties are present. These problems were attributed merely to the test implementation. METHODS: We analysed the order of the observations as an alternative explanation of the problem of ties. Using a data-set of 1393 intensive care unit (ICU) patients we performed the Hosmer-Lemeshow test with all possible subjects dispositions. RESULTS: We obtained about one million different P values, ranging from 0.01 to 0.95. DISCUSSION: It is already known that when the Hosmer-Lemeshow goodness-of-fit test is performed with a number of covariate patterns lower than the number of subjects, its result may be inaccurate. We showed that the extent of this problem could be relevant under particular conditions. We also suggest a strategy for estimating the extent of the problem and subsequent interpretation.

Hospital Mortality↗
PubMed2001

Diagnosing coeliac disease by rectal gluten challenge: a prospective study based on immunopathology, computerized image analysis and logistic regression analysis.

The purpose of this study was to evaluate the use of rectal gluten challenge in the diagnosis of coeliac disease. A total of 103 patients with features suggestive of this diagnosis were prospectively enrolled into the study; a diagnosis of coeliac disease was based on strictly defined criteria used in judging the proximal jejunal biopsy. On that basis, 45 out of the 103 patients were deemed to have coeliac disease. A slurry of gluten powder in physiological saline was introduced into the rectum, and biopsies taken before and at 2 h or 4 h after the challenge were examined immunohistochemically by computerized image analysis. Cell counts were analysed by logistic regression, and the best equations were obtained for each challenge group. The 2 h challenge yielded diagnostic sensitivity and specificity of 69.6% and 78.6% respectively. The 4 h challenge provided sensitivity and specificity of 100% and 100% respectively. These results were compared with other clinical diagnostic predictors,including anti-endomysial antibodies, which yielded diagnostic sensitivity and specificity of 70% and 98% respectively. It is concluded that a 4 h rectal challenge is a highly sensitive means of identifying gluten-sensitized individuals, and would be of particular value in cases showing negative antibody screening or equivocal biopsy appearances.

Adult↗
PubMed2001

Logistic regression model: an assessment of variability of predictions.

Risk prediction models available for cardiovascular prevention are statistical or based on machine learning methods. This paper investigates whether the logistic regression method can be considered as reference for validation of other methods. In order to test the stability of the predictions using this method, we performed two types of analyses on 50 random training and test samples drawn from the same database. In first analyses three models were obtained by forced entry of different sets of four variables. In second analyses, models were built with increasing number of predictive variables. The predictive performance was assessed by the area under the ROC curve. Although across-samples variability is low for a given model, it is large enough to lead to wrong conclusions when comparing different prediction methods. We also suggest that a low events-per-variable ratio alters the stability of a model's coefficients but does not affect the variability of prediction performance.

Area Under Curve↗