Live Crown Ratio as an Indicator for Tree Vigor and Stability of Turkey Oak (Quercus cerris L.): A Case Study

Stability of forests represents a significant objective for climate change mitigation. As stand stability is influenced by the stability of individual trees, promoting stable trees is vital for a sustainable forest management. However, inside stands, trees experience intense competition. As a result, the crown recedes and diameter growth is affected, the trees becoming slender and more susceptible to biotic and abiotic disturbances. Finding effective indicators for tree vigor and stability is therefore important. This study aimed to assess the performance of the live crown ratio as an indicator in deciding the timing of tending operations for obtaining and maintaining vigorous Turkey oak trees. Live crown ratio (LCR) and height to diameter ratio (HDR) were determined for 80 sampled Turkey oak trees. A threshold of 100 for HDR was chosen to classify trees as slender or not slender. Next, conditional inference tree and logistic regression were used to determine the LCR threshold value where trees become slender. As the sample included small trees, using breast height to measure diameter may have affected the results. Therefore, small and large trees were also analyzed separately. For the entire dataset, the methods reached quite different results (LCR threshold of 0.371 for conditional inference tree and of 0.434 for the logistic regression), and relatively high values compared to the literature. For tall trees (height > 12.5 m), the methods reached similar results: 0.386 for the conditional inference tree and 0.382 for the logistic regression. For small trees (height < 12.5 m), the conditional inference tree method could not calculate any LCR threshold estimate, while the one from the logistic regression was unrealistically large (0.628). This confirms that using DBH for small trees to compute slenderness brings systematic errors. The live crown ratio was a good indicator of growth vigor for Turkey oak trees. Therefore, for stable trees (HDR < 100), a LCR of 0.36–0.39 must be maintained and could be used to decide the timing for thinning in Turkey oak stands.

Using conditional inference to quantify interaction effects of socio-demographic covariates of US COVID-19 vaccine hesitancy

COVID-19 vaccine hesitancy has become a major issue in the U.S. as vaccine supply has outstripped demand and vaccination rates slow down. At least one recent global survey has sought to study the covariates of vaccine acceptance, but an inferential model that makes simultaneous use of several socio-demographic variables has been lacking. In this article, we present such a model using US-based survey data collected by Gallup. Our study agrees with the global survey results in some respects, but is also found to exhibit significant differences. For example, women and people aged between 25-54 were found to be more vaccine hesitant. Our conditional inference tree model suggests that trust in government, age and ethnicity are the most important covariates for predicting vaccine hesitancy, and can interact in ways that make them useful for communication-based outreach, especially if conjoined with census data. In particular, we found that the most vaccine hesitant individuals were those who identified as Black Republicans with a high school (or lower) education and lower income levels, who were involuntarily unemployed and trusted in the Trump administration.

Evaluation of algorithms using administrative health and structured electronic medical record data to determine breast and colorectal cancer recurrence in a Canadian province

Background Algorithms that use administrative health and electronic medical record (EMR) data to determine cancer recurrence have the potential to replace chart reviews. This study evaluated algorithms to determine breast and colorectal cancer recurrence in a Canadian province with a universal health care system. Methods Individuals diagnosed with stage I-III breast or colorectal cancer diagnosed from 2004 to 2012 in Manitoba, Canada were included. Pre-specified and conditional inference tree algorithms using administrative health and structured EMR data were developed. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) correct classification, and scaled Brier scores were measured. Results The weighted pre-specified variable algorithm for the breast cancer validation cohort (N = 1181, 167 recurrences) demonstrated 81.1% sensitivity, 93.2% specificity, 61.4% PPV, 97.4% NPV, 91.8% correct classification, and scaled Brier score of 0.21. The weighted conditional inference tree algorithm demonstrated 68.5% sensitivity, 97.0% specificity, 75.4% PPV, 95.8% NPV, 93.6% correct classification, and scaled Brier score of 0.39. The weighted pre-specified variable algorithm for the colorectal validation cohort (N = 693, 136 recurrences) demonstrated 77.7% sensitivity, 92.8% specificity, 70.7% PPV, 94.9% NPV, 90.1% correct classification, and scaled Brier score of 0.33. The conditional inference tree algorithm demonstrated 62.6% sensitivity, 97.8% specificity, 86.4% PPV, 92.2% NPV, 91.4% correct classification, and scaled Brier score of 0.42. Conclusions Algorithms developed in this study using administrative health and structured EMR data to determine breast and colorectal cancer recurrence had moderate sensitivity and PPV, high specificity, NPV, and correct classification, but low accuracy. The accuracy is similar to other algorithms developed to classify recurrence only (i.e., distinguished from second primary) and inferior to algorithms that do not make this distinction. The accuracy of algorithms for determining cancer recurrence only must improve before replacing chart reviews.

Transarterial Chemoembolization in Treatment-Naïve and Recurrent Hepatocellular Carcinoma: A Propensity-Matched Outcome and Risk Signature Analysis

ObjectivesThe purpose of this study was to evaluate the efficacy and safety of transarterial chemoembolization (TACE) in the treatment of patients with treatment-naïve hepatocellular carcinoma (TN-HCC) and recurrent HCC (R-HCC). In addition, risk signature analysis was performed to accurately assess patients’ recurrence and survival.MethodsThis retrospective study assessed the consecutive medical records of TN-HCC and R-HCC patients from January 2014 to December 2018. In order to reduce the patient selection bias, propensity score matching (PSM) analysis was applied. Conditional inference tree was used to establish a risk signature.ResultsA total of 401 eligible patients were included in our study, including 346 patients in the TN-HCC group and 55 patients in the R-HCC group. Forty-seven pairs of patients were chosen after the PSM analysis. Before the PSM analysis, the objective tumor regression (ORR) and disease control rate (DCR) of R-HCC patients were better than that of TN-HCC patients; however, after the PSM analysis, there was no significant difference in the ORR and DCR between the two groups (P&gt;0.05). Before the PSM analysis, the median overall survival (OS) and progression-free survival (PFS) in the R-HCC group were significantly greater than those of the TN-HCC group (OS: 24 months vs. 18 months, P =0.004; PFS: 9 months vs. 6 months, P =0.012). However, after the PSM analysis, the median OS and PFS in the R-HCC group were inferior to those in the TN-HCC group (OS: 24 months vs. 33 months, P= 0.0035; PFS: 10 months vs. 12 months, P = 0.01). The conditional inference tree divided patients into different subgroups according to tumor size, BCLC stage, and TACE sessions and shared different hazards ratio to recurrence or survival.ConclusionPatients with R-HCC treated with TACE achieved satisfactory results, although survival after the PSM analysis was not as good as in the TN-HCC group. In addition, risk signature based on conditional inference tree analysis can more accurately predict the recurrence and survival in both groups of patients.

Factors Associated With Calling 911 for an Overdose: An Ethnographic Decision Tree Modeling Approach

Objectives. To identify factors that influence when people who use drugs (PWUDs) call 911 for an overdose. Methods. We conducted 45 qualitative interviews and 180 surveys with PWUDs who had recently witnessed overdoses in Southern California from 2017 to 2019. We used conditional inference tree and random forest models to generate and validate a model to predict whether 911 would be called. Results. Our model had good in- (83%) and out-of-sample (84%) predictive accuracy. Three aspects of the social and policy environment influenced calling 911 for an overdose: the effectiveness of response strategies employed, the behavior of other bystanders, and whether the responder believes it is their responsibility to call. Conclusions. Even in the presence of policies that provide some protections, PWUDs are faced with difficult decisions about calling 911 and must weigh their own safety against that of an overdose victim. Potential interventions include strengthening training and safety planning for PWUDs, bolstering protections for PWUDs when they call 911, and separating law enforcement response from emergency medical response to overdoses. (Am J Public Health. Published online ahead of print May 20, 2021: e1–e3. https://doi.org/10.2105/AJPH.2021.306261 )

Rising Stars and Underdogs: The Role Race and Parental Education Play in Predicting Mentorship

Research suggests that youth with more financial and social resources are more likely to have access to mentorship. Conversely, the rising star hypothesis posits that youth who show promise through their individual successes are more likely to be mentored. Utilizing a nationally representative sample ( N = 4,882), we tested whether demographic characteristics (e.g., race, SES) or personal resources (e.g., academic/social success) are better predictors of receiving mentorship. Regression analyses suggested that demographic, contextual, and individual characteristics all significantly predicted access to mentorship, specifically by non-familial mentors. However, conditional inference tree models that explored the interaction of mentorship predictors by race showed that individual characteristics mattered less for Black and Latino/a youth. Therefore, the rising star hypothesis may hold true for White youth, but the story of mentoring is more complicated for youth of color. Findings highlight the implications of Critical Race Theory for mentoring research and practice.

Effect of Postoperative Radiotherapy in Thymoma Patients: A SEER-Based Study

<b><i>Introduction:</i></b> The role of postoperative radiation therapy (PORT) for thymoma is under debate, especially in patients aged ≥60 years with an advanced stage (Masaoka stages III and IV). We aimed to evaluate the efficacy of PORT for thymoma in a population-based registry. <b><i>Methods:</i></b> A retrospective analysis of the Surveillance, Epidemiology, and End Results (SEER) database was conducted to compare the outcomes of thymoma patients with or without PORT. The primary outcomes were overall survival (OS) and cancer-specific survival (CSS). Conditional inference tree analyses were performed for risk classification according to the study variables. Cox regression was performed to evaluate the prognostic effect of PORT in the specific subgroups. <b><i>Results:</i></b> A total of 2,236 patients were included. The conditional inference tree analysis identified that an age ≥60, a Masaoka stage ≥3, and the year of diagnosis were important factors when classifying patients into prognostic subgroups. PORT was found to be a protective predictor of OS in patients aged ≥60 years, those with a Masaoka stage III—IV, and those diagnosed after 2005. Further subgroup analyses revealed that PORT was significantly associated with a better OS (HR = 0.77) in patients aged ≥60 years, whereas it was not significantly associated with CSS. <b><i>Conclusions:</i></b> An older age (≥60 years) is critical for predicting survival outcomes in thymoma patients. Moreover, patients aged ≥60 years could benefit from PORT in terms of OS.

Evaluation of algorithms using administrative health and structured electronic medical record data to determine breast and colorectal cancer recurrence in a Canadian province

Background: Algorithms that use administrative health and electronic medical record (EMR) data to determine cancer recurrence have the potential to replace chart reviews. This study evaluated algorithms to determine breast and colorectal cancer recurrence in a Canadian province with a universal health care system.Methods: Individuals diagnosed with stage I-III breast or colorectal cancer diagnosed from 2004 to 2012 in Manitoba, Canada were included. Pre-specified and conditional inference tree algorithms using administrative health and structured EMR data were developed. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) correct classification, and scaled Brier scores were measured.Results: The weighted pre-specified variable algorithm for the breast cancer validation cohort (N=1181, 167 recurrences) demonstrated 81.1% sensitivity, 93.2% specificity, 61.4% PPV, 97.4% NPV, 91.8% correct classification, and scaled Brier score of 0.21. The weighted conditional inference tree algorithm demonstrated 68.5% sensitivity, 97.0% specificity, 75.4% PPV, 95.8% NPV, 93.6% correct classification, and scaled Brier score of 0.39. The weighted pre-specified variable algorithm for the colorectal validation cohort (N=693, 136 recurrences) demonstrated 77.7% sensitivity, 92.8% specificity, 70.7% PPV, 94.9% NPV, 90.1% correct classification, and scaled Brier score of 0.33. The conditional inference tree algorithm demonstrated 62.6% sensitivity, 97.8% specificity, 86.4% PPV, 92.2% NPV, 91.4% correct classification, and scaled Brier score of 0.42. Conclusions: Algorithms using administrative health and structured EMR data to determine breast and colorectal cancer recurrence had moderate sensitivity and PPV, high specificity, NPV, and correct classification, but low accuracy. Algorithms for determining cancer recurrence must improve before replacing chart reviews.