Do Rule-Derived Labels Improve Prediction When Reported Labels Are Limited? A Two-Site Machine Learning Study

**Background.** Rule derived (weak) labels can enlarge a training set when reported labels are scarce. Their value depends on label quality, on how the extra records shift the training distribution, and on whether the comparison accounts for reported labels set aside for tuning or calibration. **Methods.** We ran an exploratory label-budget experiment on *Escherichia coli* urine cultures from two de-identified electronic health record datasets: Stanford ARMD and UT Southwestern ARMD-UTSW. The task was to predict reported non-susceptibility to cefepime, cefoxitin and ceftazidime (Stanford) and

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