Data Anomalies on Model Integrity

Imagine you have a logistic regression model that heavily relies on a particular variable. This variable typically has values like 50.00, 100.00, 40.00, etc. Now, consider that due to a data quality problem, some values of this variable have lost their decimal points (e.g., 100.00 became 10000). Would this affect the model's validity? Why or why not? How would you go about correcting the model?