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Results: The interviewers overall correctly diagnosed 72 of 75 individuals.
The final model had a statistically significant predictive power (χ2 = 61.745, p < 0.0001; Hosmer and Lemeshow goodness of fit χ2 = 6.910, p = 0.546) and overall correctly classified 83.7% of the patients.
We found the support vector classifier trained on the 500 highest-ranked markers to have the greatest predictive power overall, correctly predicting the SMP response of 71.7% of the segregants on average for the SMPs considered (Figure 1).
The number of overall correctly assessed patients with regard to resectability was 11 true positive, 1 false positive, and 3 true negative.
In particular, the qualitative behavior over time including different behavior for different nodes is overall correctly simulated and agrees with the experimental observations such as the number of surviving cells or cytochrome c release.
These analyses revealed that the thickness of the temporal poles, but not inferior temporal gyri, was indeed associated with the proportion of overall correctly named objects, but not with the domain index scores.
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The overall model correctly classified 80% of the sample as accepters or nonaccepters of the vaccination for their child.
Evidence for ANS discrimination of emotion was found via PCA with 44.6% of overall observations correctly classified into the predicted emotion conditions, using ANS variables (z = 16.05, p < .001).001
The overall topology correctly reflected the basic arrangement of basal angiosperms, lower eudicots, rosids and asterids, but within each of these broader phylogenetic groupings the ordinal relationships varied appreciably from APG III (Figure 1; Inset Figure).
Overall, participants correctly identified probes that were part of the memoranda (Hits) on 68.74% (SD = 17.41) of the trials with anxiety-inducing distracters, 74.18% (SD = 11.96) of the trials with neutral distracters, and 73.75% (SD = 12.04) of the trials with scrambled distracters.
Overall % Correctly Classified: 95.7% (n = 22/23) * ΔSVI and ΔsBP/LVESVI identified as unique predictors by discriminant function analysis Structure coefficients: ΔSVI = 0.81, ΔsBP/LVESVI = 0.46 ΔSVI and ΔsBP/LVESVI simultaneous entry with p ≤ 0.15 for inclusion and p ≤ 0.20 for model retention.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com