Hi @trberg, I've started looking at the data, and reviewed the Task 1 Gold Standard workflow, and have some questions. Please bear with the long detail, as I'm trying to describe some nuances that are significant obstacles for me: 1. The instructions say: "outcome - patients are considered true positives if they have a hospitalized_visit_start_date within 5 weeks (35 days) of the outpatient_visit_start_date. Patients whose only hospitalized_visit_start_date occurs on the same day as the outpatient_visit_start_date, will not be evaluated. **Patients who have their hospitalized_visit_start_date on the same day as the outpatient_visit_start_date, but who have a separate hospitalized_visit_start_date within 5 weeks of the outpatient_visit_start_date are considered true positives.**" Can you explain your rationale for this last part that I bolded? If someone was admitted to the hospital on the same day as the outpatient visit, they probably were sick enough to concern the outpatient provider and were sent to emergency/inpatient services. If they were discharged from emergency/inpatient, then re-admitted later, I would think the most likely scenarios were: a) they recovered enough from COVID-19 in the inpatient setting to be discharged, but later worsened and were readmitted; b) they recovered in-hospital and were discharged but were later admitted for something else \- either non-COVID or sequelae of COVID (the gold-standard workflow does not appear to check if the hospitalization is COVID-related); or c) the patient was transferred to a different hospital (maybe this is fixed with macrovisits?). None of these 3 scenarios as outcomes seem helpful in deciding whether patients presenting at an outpatient visit go on to being hospitalized for COVID-19. For this reason, it would seem to me to make more sense to drop any patient hospitalized on the same day as the outpatient visit, and not be looking for a second visit. Further, I'm concerned we will be building models to predict hospitalization not just for COVID-19, but often on whether they have other conditions that predispose them to hospitalization regardless of COVID-19 status. I'm thinking there might be many instances of children needing procedures that require hospitalization, they get an outpatient test prior to admission per policy, some show positivity, they wait until they test negative, and then get their procedure done within the 35-day window. I'm guessing that the patients hospitalized on an outpatient day are probably more likely to be COVID-19-related than those we use for our actual outcome. Did you check the distribution of COVID-19 diagnoses and procedures within hospitalization to see if these hospitalizations are for COVID-19? 2. It seems from your response to this [thread](https://www.synapse.org/#!Synapse:syn25875374/discussion/threadId=8421) that our training models, if they are to be helpful in predicting the test data, should look at all patient data before and including the date of the outpatient visit subsequent to first positive COVID-19 test, and not afterwards. Presumably, this is so we can have the advantage of whatever information is available from that outpatient visit plus past history. If that is the case, will we also have access to the fact of a hospitalized visit that starts on the same day as the outpatient visit? After all, it overlaps the observation period. If so, given the evaluation criteria discussed above in 1, we should assign a 100% confidence of the outcome as you will not be evaluating patients who do not have subsequent hospitalizations: every patient with an immediate hospitalization who makes it into your evaluation set has to have a positive outcome! Correct me if I'm wrong, but this seems like a not-good immortal time bias to have baked into the outcome. Even if you withhold inpatient data for that day from our models, it seems likely that there could be enough evidence in the outpatient data to infer whether there was a same-day inpatient visit -- like an O~2~ saturation of 85. 3. The instructions say, "For your final submission, we are expecting that you submit a full workbook that takes in training data in standard OMOP format as well as testing data in the same OMOP format. Your code should ingest the OMOP tables, train a model, ingest the testing data, and then finally output a prediction file that has one prediction (a continuous score between 0 and 1) for each person_id in the testing data person table. " Given our training data allows us to peek into the future, can you describe how the test data will be given so that cannot happen? Can you specifically describe how our workflow should process the test data? Do we need our own version of the gold-standard workflow to not look into the future? Will there be two different person tables for train and test? What about all the other tables? I'm trying to architect the workflow, and this is really important to know this at the beginning stages. 4. Will you be scheduling a kick-off call for other questions and answers? Many thanks, @christophe.lambert