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whonet version 5 6  (STATA Corporation)


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    STATA Corporation whonet version 5 6
    Whonet Version 5 6, supplied by STATA Corporation, used in various techniques. Bioz Stars score: 99/100, based on 36797 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/stata+6+0/STATA+12%2E0/pmc13028962-87-9-7
    Average 99 stars, based on 36797 article reviews
    whonet version 5 6 - by Bioz Stars, 2026-09
    99/100 stars

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    Article Title: Self-Reported HIV Antibody Testing among Latino Urban Day Laborers
    Article Snippet: The variable with the larger odds ratio in a univariate analysis with the outcome variable of HIV testing in the last 12 months was the one that was subsequently used in the multivariate model. Stata 6.0 was used for the analysis (StataCorp LP, College Station, Texas).

    Article Title: Discrepancy between objective and subjective measures of job stress and sickness absence.
    Article Snippet: Poisson regressions were conducted using Stata 6.0 (Stata Corp, College Station, TX, USA), age standardization was done using R 1.6.2 (20).

    Article Title: Female polyandry affects their sons' reproductive success in the red flour beetle Tribolium castaneum.
    Article Snippet: Data were analysed using S-Plus 4.5 (MathSoft Inc., Cambridge, MA, USA) and Stata 6.0 (Stata Corporation, College Station, TX, USA).

    Article Title: Ecological Genetics of Stipa pulchra in Environmental Restoration
    Article Snippet: All analyses were conducted with Stata 6.0 (StataC orp 1999).

    Article Title: Changes in the prescribing pattern of antidepressant drugs in elderly patients: an Italian, nationwide, population-based study.
    Article Snippet: Background Despite the high use of antidepressants (ADs) among the elderly, there is limited information about the prescribing pattern of these drugs in the Italian elderly population.. The aim of this study was to analyze the trend in the use of ADs in the Italian elderly patients in the years 2003-2009, and specifically, to evaluate rates and predictors of AD treatment discontinuation in depressed older patients.. Methods The nationwide general practice Health Search Database (HSD) was used to identify AD users aged 65 years old and over from 2003 to 2009.

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    Article Title: Machine learning approaches for risk prediction after percutaneous coronary intervention: a systematic review and meta-analysis
    Article Snippet: .. Resnic et al. , LR, NN , Simplified prognostic risk scoring systems , 20 hidden nodes , Used forward and backward stepwise selection for model construction Excluded variables with high rates of missing data Conducted robustness checks on models without missing data adjustments Focused on maintaining a complete data set for key analysis variables , 67% training and 33% testing , ROC AUC: provided as C -statistic , NR , LR In-hospital mortality: AUC 0.85 In-hospital death, myocardial infarction, or same-day bypass surgery (MACE): AUC 0.78 NN In-hospital mortality: AUC 0.83 In-hospital death, myocardial infarction, or same-day bypass surgery (MACE): AUC 0.81 , LR Hosmer–Lemeshow In-hospital mortality: 13.060 NN Hosmer–Lemeshow In-hospital mortality: 7.170 Simplified prognostic risk scoring systems Hosmer–Lemeshow In-hospital mortality: 8.86 , Single random split (random split into training and test sets) , STATA 6.0 for multiple LR models and risk score models Goodman’s NetProv4 software for feedforward ANNs. .. Sampedro et al. , RF, extreme randomized trees, GB, SVM classifier, L2-regularized LR, non-regularized LR (LR_NOREG) , PRospective Evaluation of non-ST-elevation myocardial infarction Treatment Strategy On outcomes (PRESTO)-1 risk score PRESTO-2 risk score EVENT risk score , 68 variables , Implemented multiple imputation techniques Applied imputation by chained equations Conducted separate imputations for each outcome Ensured consistency across imputed data sets , NR , ROC AUC: provided as C -statistic PR AUC: For ML model and clinical scores. Other metrics: sensitivity, specificity, precision, NPV at different thresholds , Data transformation or feature scaling: one-hot encoding for multi-category features Missing data imputation: median and mode imputation for missing values Over-sampling: use of k -fold cross-validation to handle imbalanced outputs , 0.77 (0.66–0.89) , No calibration , Cross-validation (10-fold cross-validation with 20 repetitions) , Python with the scikit-learn library for implementing k -fold splitting, feature selection, and ML classifiers.

    Software:

    Article Title: Machine learning approaches for risk prediction after percutaneous coronary intervention: a systematic review and meta-analysis
    Article Snippet: .. Resnic et al. , LR, NN , Simplified prognostic risk scoring systems , 20 hidden nodes , Used forward and backward stepwise selection for model construction Excluded variables with high rates of missing data Conducted robustness checks on models without missing data adjustments Focused on maintaining a complete data set for key analysis variables , 67% training and 33% testing , ROC AUC: provided as C -statistic , NR , LR In-hospital mortality: AUC 0.85 In-hospital death, myocardial infarction, or same-day bypass surgery (MACE): AUC 0.78 NN In-hospital mortality: AUC 0.83 In-hospital death, myocardial infarction, or same-day bypass surgery (MACE): AUC 0.81 , LR Hosmer–Lemeshow In-hospital mortality: 13.060 NN Hosmer–Lemeshow In-hospital mortality: 7.170 Simplified prognostic risk scoring systems Hosmer–Lemeshow In-hospital mortality: 8.86 , Single random split (random split into training and test sets) , STATA 6.0 for multiple LR models and risk score models Goodman’s NetProv4 software for feedforward ANNs. .. Sampedro et al. , RF, extreme randomized trees, GB, SVM classifier, L2-regularized LR, non-regularized LR (LR_NOREG) , PRospective Evaluation of non-ST-elevation myocardial infarction Treatment Strategy On outcomes (PRESTO)-1 risk score PRESTO-2 risk score EVENT risk score , 68 variables , Implemented multiple imputation techniques Applied imputation by chained equations Conducted separate imputations for each outcome Ensured consistency across imputed data sets , NR , ROC AUC: provided as C -statistic PR AUC: For ML model and clinical scores. Other metrics: sensitivity, specificity, precision, NPV at different thresholds , Data transformation or feature scaling: one-hot encoding for multi-category features Missing data imputation: median and mode imputation for missing values Over-sampling: use of k -fold cross-validation to handle imbalanced outputs , 0.77 (0.66–0.89) , No calibration , Cross-validation (10-fold cross-validation with 20 repetitions) , Python with the scikit-learn library for implementing k -fold splitting, feature selection, and ML classifiers.



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