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Conclusion: The previously developed CABDEAL model was predictive of increased morbidity in the SPARCS database.
Model parameters were calculated from isothermal DSC experiments, and the model was predictive of monomer conversion in non-isothermal conditions in presence of single initiators and the mixture of them.
When these variables were included in a logistic regression model that controlled for the body mass index, the study centre, methods of proximal and distal locking configuration and fracture type, the model was predictive (P = .009) of full weight bearing ability.
The model was predictive for the major types of NSCLC – ADC and SCC, and performed reasonably satisfactory in two independent patient cohorts.
These models were then used to assess whether elevated EGFR copy number, as defined in each model, was predictive for cetuximab efficacy.
The dominant model was predictive of breast cancer risk compared to controls for all breast cancer patients (OR 1.4, 95% CI 1.1-1.8 1.1-1.8
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The model is predictive and only requires the structure of the polymers and the existing group parameters in the calculation.
The stability test of the model with regard to data division was found to be positive, indicating that the generated model is predictive.
This allows to deduce that the model is predictive (Figs. 16 and 17).
This is expected to provide a more efficient nonconformity measure than absolute difference, provided that the error model is predictive.
There is still a debate in the QSAR community on the best ways to verify whether the model is predictive or not.
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CEO of Professional Science Editing for Scientists @ prosciediting.com