scholarly journals Testing for Frequency and Structural Effects in an English Stress Shift

2010 ◽  
Vol 36 (1) ◽  
pp. 411 ◽  
Author(s):  
Morgan Sonderegger
Keyword(s):  
Logistic Regression ◽  
Language Change ◽  
Frequency Effect ◽  
Multiple Logistic Regression ◽  
Structural Effects ◽  
Conditioning Factors ◽  
Phonological Structure ◽  
Initial Syllable ◽  
As Effects

In lieu of an abstract, here is a brief excerpt:This paper considers the English diatonic stress shift (DSS). We examine the role of frequency and phonological structure as conditioning factors for which of a set of noun/verb pairs have undergone the DSS between 1700 and the present. Previous work by Phillips (1984) has shown a role of frequency: on average, words which have undergone the DSS have lower frequency than those which have not. Using a new dataset, we show via multiple logistic regression that there is a significant effect of frequency in the direction shown by Phillips, as well as effects of phonological structure; for example, a closed initial syllable makes change more likely. There is also a strong interaction between the effects of frequency and structure; in particular, structure modulates the strength and direction of the frequency effect. Our use of multiple regression follows its widespread use in sociolinguistics (e.g., Labov 1994) for quantifying the relative effects of different conditioning factors in cases of language change.

scholarly journals Smoking Cessation Intention and Its Association with Advice to Quit from Significant Others and Medical Professionals

2021 ◽  
Vol 18 (6) ◽  
pp. 2899
Author(s):  
Jun Hyun Hwang ◽  
Soon-Woo Park
Keyword(s):  
Smoking Cessation ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Multiple Logistic Regression Analysis ◽  
Significant Others ◽  
Intention To Quit ◽  
Medical Professionals ◽  
Multiple Logistic Regression ◽  
Quit Smoking

Few studies have simultaneously considered the effects of significant others and medical professionals’ advice to quit smoking on smoking cessation intention. The present study involved 3841 current adult Korean smokers, divided into four groups with an intention to quit within 1 month, within 6 months, someday, and without intention to quit. Multinomial multiple logistic regression analysis was conducted according to smoking cessation intention level, adjusted for potential confounders, including past smoking cessation attempts. Smokers who had been advised to quit smoking by both significant others and medical professionals, significant others only, and medical professionals only were 2.63 (95% confidence interval (CI): 1.62–4.29), 1.84 (95% CI: 1.17–2.89), and 1.44 (95% CI: 0.70–2.94) times more likely to intend to quit within 1 month, respectively, than those who were not advised to quit. The odds ratios of an intention to quit within 6 months were 2.91 (95% CI: 1.87–4.54), 2.49 (95% CI: 1.69–3.68), and 0.94 (95% CI: 0.44–2.05), respectively. To promote smokers’ intention to quit, the role of significant others should be considered. Medical professionals’ advice to quit smoking remains important, increasing the effects of significant others’ advice.

Orthographic constraints and frequency effects in complex word identification

2004 ◽  
Vol 7 (1) ◽  
pp. 49-59 ◽  
Author(s):  
Ludo Verhoeven ◽  
R. H. Baayen ◽  
Robert Schreuder
Keyword(s):  
Word Frequency ◽  
Word Identification ◽  
Frequency Effect ◽  
Adult Readers ◽  
Word Decoding ◽  
Strong Negative Correlation ◽  
Initial Syllable ◽  
Dual Route ◽  
Dual Route Model

In an experimental study we explored the role of word frequency and orthographic constraints in the reading of Dutch bisyllabic words. Although Dutch orthography is highly regular, several deviations from a one-to-one correspondence occur. In polysyllabic words, the grapheme E may represent three different vowels: /ε /, /e/, or /œ /. In the experiment, skilled adult readers were presented lists of bisyllabic words containing the vowel E in the initial syllable and the same grapheme or another vowel in the second syllable. We expected word frequency to be related to word latency scores. On the basis of general word frequency data, we also expected the interpretation of the initial syllable as a stressed /e/ to be facilitated as compared to the interpretation of an unstressed /œ /. We found a strong negative correlation between word frequency and latency scores. Moreover, for words with E in either syllable we found a preference for a stressed /e/ interpretation, indicating a lexical frequency effect. The results are discussed with reference to a parallel dual-route model of word decoding.

scholarly journals Serum Procalcitonin as a Diagnostic Marker for Systemic Inflammatory Response Syndrome in Intensive Care Unit Patients

2020 ◽  
Author(s):  
Rama Mishra Ramapriya ◽  
Pallavi Prakash
Keyword(s):  
Intensive Care Unit ◽  
Logistic Regression ◽  
Intensive Care ◽  
Systemic Inflammatory Response Syndrome ◽  
Inflammatory Response ◽  
Predictive Value ◽  
Systemic Inflammatory Response ◽  
Multiple Logistic Regression ◽  
Early Assessment

Introduction: Early assessment of Systemic Inflammatory Response Syndrome (SIRS) through various biomarkers like Procalcitonin (PCT), C-reactive Protein (CRP), Interleukin-1 (IL-1) etc., is crucial to manage the outcome of patients. Levels of PCT concerning its likelihood to distinguish patients with SIRS and non-SIRS and the possibility to predict mortality in patients with sepsis has been variable. Aim: To investigate the role of PCT in early diagnosis of sepsis in patients admitted to Intensive Care Unit (ICU). Materials and Methods: In this prospective observational study, 136 patients hospitalised in ICU at Vydehi Institute of Medical Sciences and Research Centre, Bangalore, Karnataka, India, between July 2019 to June 2020 were evaluated and PCT was analysed using Finecare™ PCT rapid test. Receiver Operating Characteristic (ROC) curve analysis and multiple logistic regression was carried out to detect the association of predictive PCT value with its outcomes. Results: PCT showed the best predictive value in the diagnosis of SIRS at 1.68 ng/mL (Area Under Curve (AUC)-0.87; p<0.05) having Positive Predictive Value (PPV) and Negative Predictive Value (NPV) of 90.43% and 73.81%, respectively. Multiple logistic regression model adjusted for age, weight, and duration of stay to predict the outcome of SIRS, positive blood culture and fatality case rate derived a significant association of PCT with Odds Ratio (OR) being 1.23 (1.11-2.31), 1.06 (1.01-1.98) and 1.76 (1.08-2.14), respectively. Conclusion: Early identification and treatment for sepsis significantly affects mortality. It appears that consecutive measurements of biomarkers could be valuable, but further prospective studies are important to characterise the role of PCT as a prognostic marker in sepsis and severe sepsis.

scholarly journals The Effects of Illness Perception on Diet Adherence in Patients With Hypertension

2021 ◽  
Vol 6 (2) ◽  
pp. 65-72
Author(s):  
Leila Rouhi Balasi ◽  
◽  
Arsalan Salari ◽  
Abdolhosein Emami Sigaroudi ◽  
Asieh Ashouri ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Multiple Logistic Regression Analysis ◽  
Illness Perception ◽  
Analysis Data ◽  
Study Data ◽  
Dietary Adherence ◽  
Multiple Logistic Regression ◽  
Cross Sectional ◽  
Diet Adherence

Background: The role of nutrition is undeniable in controlling hypertension; diet is among the most effective non-pharmaceutical methods. The current study aimed to determine the role of illness perception on diet adherence in patients with hypertension. Materials & Methods: This cross-sectional study examined 268 patients with hypertension. The study sample was selected by convenience sampling method. The study tool consisted of the patients’ individual, social, and clinical factors, illness perception about hypertension, and adherence to the diet. The necessary data were analyzed using multiple logistic regression models. Results: The Mean±SD score of illness perception was measured as 37.09±4.91 out of 56. Adherence to the recommended diet was relatively desirable in the majority of the examined patients (62%). Multiple logistic regression analysis data revealed no significant relationship between the scores of illness perception and dietary adherence (Adjusted OR=1.038, 95%CI: 0.974-1.105, P =0.250). The main predictor of dietary adherence was having hypertension dietary knowledge (OR=2.198, 95%CI: 1.198-4.035, P=0.011). Conclusion: Our study data revealed that increasing awareness among patients with hypertension complications can improve self-care behaviors, including adherence to standard diets. Therefore, emphasis on increasing awareness among these patients and their continued follow-up seems necessar

scholarly journals Role of Serum E-Selectin as a Biomarker of Infection Severity in Coronavirus Disease 2019

2021 ◽  
Vol 10 (17) ◽  
pp. 4018
Author(s):  
Alessandra Oliva ◽  
Emanuele Rando ◽  
Dania Al Ismail ◽  
Massimiliano De Angelis ◽  
Francesca Cancelli ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Thrombotic Event ◽  
Endothelial Activation ◽  
Multiple Logistic Regression ◽  
Logistic Regression Models ◽  
Icu Admission ◽  
Increased Risk ◽  
Single Center Study

Introduction: E-selectin is a recognized marker of endothelial activation; however, its place in Coronavirus Disease 2019 (COVID-19) has not been fully explored. Aims of the study are to compare sE-selectin values among the Intensive Care Unit (ICU)-admitted and non-admitted, survived and non-survived patients and those with or without thrombosis. Methods: A single-center study of patients with COVID-19 hospitalized at Policlinico Umberto I (Rome) from March to May 2020 was performed. Simple and multiple logistic regression models were developed. Results: One hundred patients were included, with a median age (IQR) of 65 years (58–78). Twenty-nine (29%) were admitted to ICU, twenty-eight (28%) died and nineteen (19%) had a thrombotic event. The median value (IQR) of sE-selectin was 26.1 ng/mL (18.1–35). sE-selectin values did not differ between deceased and survivors (p = 0.06) and among patients with or without a thrombotic event (p = 0.22). Compared with patients who did not receive ICU treatments, patients requiring ICU care had higher levels of sE-selectin (36.6 vs. 24.1 ng/mL; p < 0.001). In the multiple logistic regression model, sE-selectin levels > 33 ng/mL, PaO2/FiO2 < 200 and PaO2/FiO2 200–300 were significantly associated with an increased risk of ICU admission. sE-selectin values significantly correlated with a neutrophil count (R = 0.32 (p = 0.001)) and the number of days from the symptoms onset to hospitalization (R = 0.28 (p = 0.004)). Conclusions: sE-selectin levels are predictive of ICU admission in COVID-19 patients. Since data on the relation between sE-selectin and COVID-19 are scarce, this study aims to contribute toward the comprehension of the pathogenic aspects of COVID-19 disease, giving a possible clinical marker able to predict its severity.

scholarly journals Data-Driven Urban Traffic Accident Analysis and Prediction Using Logit and Machine Learning-Based Pattern Recognition Models

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Vahid Najafi Moghaddam Gilani ◽  
Seyed Mohsen Hosseinian ◽  
Meisam Ghasedi ◽  
Mohammad Nikookar
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Logistic Regression ◽  
Logit Model ◽  
Dominant Role ◽  
Poor Quality ◽  
Urban Traffic ◽  
Multiple Logistic Regression ◽  
Artificial Neural Network Ann

Modeling the severity of accidents based on the most effective variables accounts for developing a high-precision model presenting the possibility of occurrence of each category of future accidents, and it could be utilized to prioritize the corrective measures for authorities. The purpose of this study is to identify the variables affecting the severity of the injury, fatal, and property damage only (PDO) accidents in Rasht city by collecting information on urban accidents from March 2019 to March 2020. In this regard, the multiple logistic regression and the pattern recognition type of artificial neural network (ANN) as a machine learning solution are used to recognize the most influential variables on the severity of accidents and the superior approach for accident prediction. Results show that the multiple logistic regression in the forward stepwise method has R2 of 0.854 and an accuracy prediction power of 89.17%. It turns out that the accidents occurred between 18 and 24 and KIA Pride vehicle has the highest effect on increasing the severity of accidents, respectively. The most important result of the logit model accentuates the role of environmental variables, including poor lighting conditions alongside unfavorable weather and the dominant role of unsafe and poor quality of vehicles on increasing the severity of accidents. In addition, the machine learning model performs significantly better and has higher prediction accuracy (98.9%) than the logit model. In addition, the ANN model’s greater power to predict and estimate future accidents is confirmed through performance and sensitivity analysis.

Determining the Most Valuable Clinical Variables : a Stepwise Multiple Logistic Regression Program

1980 ◽  
Vol 19 (01) ◽  
pp. 42-49 ◽  
Author(s):  
B. W. Brown ◽  
C. Engelhard ◽  
J. Haipern ◽  
J. F. Fries ◽  
L. S. Coles
Keyword(s):  
Logistic Regression ◽  
Predictor Variable ◽  
Clinical Problem ◽  
Relevant Information ◽  
Stepwise Logistic Regression ◽  
Multiple Logistic Regression ◽  
Binary Outcome ◽  
Medical Problems ◽  
Valuable Predictor ◽  
Data Banks

In solving a clinical problem of diagnosis, prognosis, or treatment choice, a physician must select from among a large group of possible tests. In general, an ordering exists specifying which tests are most valuable in providing relevant information concerning the problem on hand. The computer program package to be described (MW) extracts appropriate data from the ARAMIS data banks and then analyzes the data by stepwise logistic regression. A binary outcome (diagnosis, prognostic event, or treatment response) is sequentially associated with possible tests, and the most powerful combination of tests is identified. For example, the most valuable predictor variable of early mortality in SLE is proteinuria, followed sequentially by anemia and absence of arthritis. Experience with these techniques suggests : 1. optimal certainty is usually reached after only three or four tests; 2. several different test sequences may lead to the same level of certainty; 3. diagnosis may usually be ascertained with greater certainty than prognosis; 4. many medical problems contain considerable non-reducible uncertainty; 5. a relatively small group of tests are typically found among the most powerful; 6. results are consistent across several patient populations; 7. results are largely independent of the particular statistic employed. These observations suggest strategies for maximizing information while minimizing risk and expense.

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