scholarly journals Marketability Probability Study of Cherry Tomato Cultivars Based on Logistic Regression Models

Agronomy ◽  
2018 ◽  
Vol 8 (9) ◽  
pp. 176 ◽  
Author(s):  
Manuel Díaz-Pérez ◽  
Ángel Carreño-Ortega ◽  
Marta Gómez-Galán ◽  
Ángel-Jesús Callejón-Ferre
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Categorical Variables ◽  
Ripe Fruit ◽  
Storage Test ◽  
Logistic Regression Models ◽  
Solanum Lycopersicum L ◽  
Fruit Cracking ◽  
Tomato Cultivars

The purpose of this study was to demonstrate interest in applying simple and multiple logistic regression analyses to the marketability probability of commercial tomato (Solanum lycopersicum L.) cultivars when the tomatoes are harvested as loose fruit. A fruit’s firmness and commercial quality (softening or over-ripe fruit, cracking, cold damage, and rotting) were determined at 0, 7, 14, and 21 days of storage. The storage test simulated typical conditions from harvest to purchase-consumption by the consumer. The combined simple and multiple analyses of the primary continuous and categorical variables with the greatest influence on the commercial quality of postharvest fruit allowed for a more detailed understanding of the behavior of different tomato cultivars and identified the cultivars with greater marketability probability. The odds ratios allowed us to determine the increase or decrease in the marketability probability when we substituted one cultivar with a reference one. Thus, for example, the marketability probability was approximately 2.59 times greater for ‘Santyplum’ than for ‘Angelle’. Overall, of the studied cultivars, ‘Santyplum’, followed by ‘Dolchettini’, showed greater marketability probability than ‘Angelle’ and ‘Genio’. In conclusion, the logistic regression model is useful for studying and identifying tomato cultivars with good postharvest marketability characteristics.

Physician Perceptions on Quality of Life and Resuscitation Preferences for Extremely Early Newborns

2021 ◽  
Author(s):  
Danielle LoRe ◽  
Christopher Mattson ◽  
Dalia M. Feltman ◽  
Jessica T. Fry ◽  
Kathleen G. Brennan ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Logistic Regression ◽  
Regression Models ◽  
Mixed Effects ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Comfort Care ◽  
Logistic Regression Models ◽  
Extremely Preterm

Objective The study aimed to explore physician views on whether extremely early newborns will have an acceptable quality of life (QOL), and if these views are associated with physician resuscitation preferences. Study Design We performed a cross-sectional survey of neonatologists and maternal fetal medicine (MFM) attendings, fellows, and residents at four U.S. medical centers exploring physician views on future QOL of extremely early newborns and physician resuscitation preferences. Mixed-effects logistic regression models examined association of perceived QOL and resuscitation preferences when adjusting for specialty, level of training, gender, and experience with ex-premature infants. Results A total of 254 of 544 (47%) physicians were responded. A minority of physicians had interacted with surviving extremely early newborns when they were ≥3 years old (23% of physicians in pediatrics/neonatology and 6% in obstetrics/MFM). The majority of physicians did not believe an extremely early newborn would have an acceptable QOL at the earliest gestational ages (11% at 22 and 23% at 23 weeks). The majority of physicians (73%) believed that having an extremely preterm infant would have negative effects on the family's QOL. Mixed-effects logistic regression models (odds ratio [OR], 95% confidence interval [CI]) revealed that physicians who believed infants would have an acceptable QOL were less likely to offer comfort care only at 22 (OR: 0.19, 95% CI: 0.05–0.65, p < 0.01) and 23 weeks (OR: 0.24, 95% CI: 0.07–0.78, p < 0.02). They were also more likely to offer active treatment only at 24 weeks (OR: 9.66, 95% CI: 2.56–38.87, p < 0.01) and 25 weeks (OR: 19.51, 95% CI: 3.33–126.72, p < 0.01). Conclusion Physician views of extremely early newborns' future QOL correlated with self-reported resuscitation preferences. Residents and obstetric physicians reported more pessimistic views on QOL. Key Points

scholarly journals Discrepancy Between Perceived Diet Quality and Actual Diet Quality Among US Adult Cancer Survivors (OR14-05-19)

2019 ◽  
Vol 3 (Supplement_1) ◽  
Author(s):  
Hong Xue ◽  
Jin Liu
Keyword(s):  
Logistic Regression ◽  
Cancer Survivors ◽  
Diet Quality ◽  
Regression Models ◽  
Vegetable Intake ◽  
Multinomial Logistic Regression ◽  
Calorie Intake ◽  
Fruit Intake ◽  
Logistic Regression Models

Abstract Objectives Postdiagnosis diet and food choices are essential for survivorship management. However, diet quality of cancer survivors is generally low. This study aimed to examine he temporal trends of diet quality of cancer survivors, assess the discrepancy between perceived diet quality and actual diet quality among cancer survivors, and its impact on their actual diet quality. Methods The National Health and Nutrition Examination Survey (NHANES) data 2005 -2014 were used in this study. Logistic regression models were fit to examine the influence of misperception of eating healthiness on diet quality measured by healthy eating index (HEI). Multinomial logistic regression models were fit to examine the factors that may influence the likelihood of diet quality misperception. Results Between 2005 and 2014, there was an increasing trend in HEI in cancer survivors, with a small but significant increase of 0.7 per year. Non-Hispanic white survivors had a consistent higher total HEI score compared to other race/ethnic groups during this period. There were 11.66%, 29.67% and 38.02% cancer survivors who rated their overall diet quality as excellent, very good, or good, while 16.96% and 3.69% perceived their overall diet as fair and poor respectively. Kappa statistic indicated a low agreement between the self-perceived diet quality and the actual diet quality measured by HEI among cancer survivors (0.06, 95% CI: 0.02, 0.09). with adjustment of age, gender, race/ethnicity, and SES status, over-rate misperception was associated with a 5.39 lower total HEI score P < 0.0001), 1.00 lower HEI score of empty calorie intake (P = 0.0028), 0.15 lower score of vegetable intake (P = 0.108) and 0.29 lower score of fruit intake. On the other hand, under-rate misperception was associated with a 7.12 higher total HEI score P < 0.0001), 2.57 higher HEI score of empty calorie intake (P < 0.0001), 0.02 higher score of vegetable intake (P = 0.904) and 0.84 higher score of fruit intake (P = 0.001). Multinomial logistic regression suggested that higher income was also associated with higher odds of being an over-rater. Individuals with college education or above were more likely to over-rate their diet quality compared to those with high school or under education (OR: 1.32, 95% CI: 1.005 1.732). Moreover, Hispanics were more likely than Non-Hispanic Whites to over-rate their diet quality ( OR: 1.792, 95% CI: 1.062, 3.024). Conclusions The divergence between self-assessed eating health and the HEI measured diet quality was an important factor that may have influenced cancer survivors’ diet behavior and diet quality. Funding Sources NA.

scholarly journals Ordinal logistic regression models: application in quality of life studies

2008 ◽  
Vol 24 (suppl 4) ◽  
pp. s581-s591 ◽  
Author(s):  
Mery Natali Silva Abreu ◽  
Arminda Lucia Siqueira ◽  
Clareci Silva Cardoso ◽  
Waleska Teixeira Caiaffa
Keyword(s):  
Quality Of Life ◽  
Logistic Regression ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Ordinal Logistic Regression ◽  
Proportional Odds Model ◽  
Outcome Variable ◽  
Proportional Odds ◽  
Logistic Regression Models

Quality of life has been increasingly emphasized in public health research in recent years. Typically, the results of quality of life are measured by means of ordinal scales. In these situations, specific statistical methods are necessary because procedures such as either dichotomization or misinformation on the distribution of the outcome variable may complicate the inferential process. Ordinal logistic regression models are appropriate in many of these situations. This article presents a review of the proportional odds model, partial proportional odds model, continuation ratio model, and stereotype model. The fit, statistical inference, and comparisons between models are illustrated with data from a study on quality of life in 273 patients with schizophrenia. All tested models showed good fit, but the proportional odds or partial proportional odds models proved to be the best choice due to the nature of the data and ease of interpretation of the results. Ordinal logistic models perform differently depending on categorization of outcome, adequacy in relation to assumptions, goodness-of-fit, and parsimony.

scholarly journals The Effect of Various Police Enforcement Actions on Violent Crime: Evidence From a Saturation Foot-Patrol Intervention

2018 ◽  
Vol 29 (6-7) ◽  
pp. 611-629 ◽  
Author(s):  
Eric L. Piza
Keyword(s):  
Quality Of Life ◽  
Logistic Regression ◽  
Crime Prevention ◽  
Regression Models ◽  
Policy Implications ◽  
Target Area ◽  
Spatial Displacement ◽  
Foot Patrol ◽  
Logistic Regression Models

The current study tests the crime prevention effect of different police actions conducted during a foot-patrol saturation initiative in Newark, New Jersey. Police actions were categorized into two typologies: enforcement actions (i.e., arrests, quality of life summonses and field interrogations) and guardian actions (i.e., business checks, citizen contacts, bus checks, and taxi inspections). Logistic regression models tested the effect of enforcement and guardian actions on crime during daily (i.e., 24-hr) periods as well as the intervention’s operational (6:00 p.m.-2:00 a.m.) and nonoperational (2:00 a.m.-5:00 p.m.) periods. Analyses were conducted twice, once for the Operation Impact target area and once for a surrounding catchment zone (to measure spatial displacement). Findings suggest that guardian actions had a greater crime prevention effect than enforcement actions on crime occurrence. Policy implications of the findings are discussed.

scholarly journals An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression

2016 ◽  
Vol 76 (6) ◽  
pp. 986-1004 ◽  
Author(s):  
Brandi A. Weiss ◽  
William Dardick
Keyword(s):  
Logistic Regression ◽  
Data Model ◽  
Regression Models ◽  
Group Membership ◽  
Mixture Modeling ◽  
Model Fit ◽  
Logistic Regression Models ◽  
Hypothetical Data ◽  
Simulation Results

This article introduces an entropy-based measure of data–model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify the quality of classification and separation of group membership. Entropy complements preexisting measures of data–model fit and provides unique information not contained in other measures. Hypothetical data scenarios, an applied example, and Monte Carlo simulation results are used to demonstrate the application of entropy in logistic regression. Entropy should be used in conjunction with other measures of data–model fit to assess how well logistic regression models classify cases into observed categories.

The impact of SPY angiography on intraoperative decision making and outcomes for post-mastectomy reconstruction

2019 ◽  
pp. 1-5
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Skin Necrosis ◽  
Regression Models ◽  
Multivariable Analysis ◽  
P Value ◽  
Flap Necrosis ◽  
Single Institution ◽  
Logistic Regression Models ◽  
The Impact

Objective: While the use of intraoperative laser angiography (SPY) is increasing in mastectomy patients, its impact in the operating room to change the type of reconstruction performed has not been well described. The purpose of this study is to investigate whether SPY angiography influences post-mastectomy reconstruction decisions and outcomes. Methods and materials: A retrospective analysis of mastectomy patients with reconstruction at a single institution was performed from 2015-2017.All patients underwent intraoperative SPY after mastectomy but prior to reconstruction. SPY results were defined as ‘good’, ‘questionable’, ‘bad’, or ‘had skin excised’. Complications within 60 days of surgery were compared between those whose SPY results did not change the type of reconstruction done versus those who did. Preoperative and intraoperative variables were entered into multivariable logistic regression models if significant at the univariate level. A p-value <0.05 was considered significant. Results: 267 mastectomies were identified, 42 underwent a change in the type of planned reconstruction due to intraoperative SPY results. Of the 42 breasts that underwent a change in reconstruction, 6 had a ‘good’ SPY result, 10 ‘questionable’, 25 ‘bad’, and 2 ‘had areas excised’ (p<0.01). After multivariable analysis, predictors of skin necrosis included patients with ‘questionable’ SPY results (p<0.01, OR: 8.1, 95%CI: 2.06 – 32.2) and smokers (p<0.01, OR:5.7, 95%CI: 1.5 – 21.2). Predictors of any complication included a change in reconstruction (p<0.05, OR:4.5, 95%CI: 1.4-14.9) and ‘questionable’ SPY result (p<0.01, OR: 4.4, 95%CI: 1.6-14.9). Conclusion: SPY angiography results strongly influence intraoperative surgical decisions regarding the type of reconstruction performed. Patients most at risk for flap necrosis and complication post-mastectomy are those with questionable SPY results.

scholarly journals Effect of prostatic apex shape (Lee types) and urethral sphincter length in preoperative MRI on very early continence rates after radical prostatectomy

2021 ◽  
Author(s):  
Mike Wenzel ◽  
Felix Preisser ◽  
Matthias Mueller ◽  
Lena H. Theissen ◽  
Maria N. Welte ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Radical Prostatectomy ◽  
Regression Models ◽  
Urethral Sphincter ◽  
Anatomical Characteristics ◽  
Logistic Regression Models ◽  
Pad Test ◽  
Type D ◽  
Multivariable Logistic Regression ◽  
Type C

Abstract Purpose To test the effect of anatomic variants of the prostatic apex overlapping the membranous urethra (Lee type classification), as well as median urethral sphincter length (USL) in preoperative multiparametric magnetic resonance imaging (mpMRI) on the very early continence in open (ORP) and robotic-assisted radical prostatectomy (RARP) patients. Methods In 128 consecutive patients (01/2018–12/2019), USL and the prostatic apex classified according to Lee types A–D in mpMRI prior to ORP or RARP were retrospectively analyzed. Uni- and multivariable logistic regression models were used to identify anatomic characteristics for very early continence rates, defined as urine loss of ≤ 1 g in the PAD-test. Results Of 128 patients with mpMRI prior to surgery, 76 (59.4%) underwent RARP vs. 52 (40.6%) ORP. In total, median USL was 15, 15 and 10 mm in the sagittal, coronal and axial dimensions. After stratification according to very early continence in the PAD-test (≤ 1 g vs. > 1 g), continent patients had significantly more frequently Lee type D (71.4 vs. 54.4%) and C (14.3 vs. 7.6%, p = 0.03). In multivariable logistic regression models, the sagittal median USL (odds ratio [OR] 1.03) and Lee type C (OR: 7.0) and D (OR: 4.9) were independent predictors for achieving very early continence in the PAD-test. Conclusion Patients’ individual anatomical characteristics in mpMRI prior to radical prostatectomy can be used to predict very early continence. Lee type C and D suggest being the most favorable anatomical characteristics. Moreover, longer sagittal median USL in mpMRI seems to improve very early continence rates.

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