Update test.py

test.py

@@ -1,3 +1,30 @@
+SECONDARY-CANCER-TYPE UPDATE RULE (no event collapse)
+
+Goal: Within any 30-day window for the same evidence_source, update only the
+`secondary_cancer_type` field; keep every assessment event separate and leave all
+other fields unchanged.
+
+How to apply:
+1) Group assessments by evidence_source (e.g., imaging, physician, radiation, etc.).
+2) Within each source, form groups where events occur within 30 calendar days of one
+   another. Use transitive closure (if A is within 30 days of B and B within 30 days of C,
+   then A,B,C are in the same group).
+3) For each group, collect all stated secondary cancer types across the group.
+   - Normalize by lowercasing and trimming; map obvious synonyms (e.g., “hepatic mets”
+     → “liver metastasis”) before de-duplication.
+4) Write the **comma-separated, alphabetical list of unique values** back into the
+   `secondary_cancer_type` field of **each event in that group**.
+5) Do **not** modify any other fields (including `date_of_disease_progression_assessment`,
+   `disease_progression_status`, `evidence_for_*`, `treatment_change`, etc.).
+6) If no secondary cancer type is present in the entire group, leave the field as `""`.
+
+Examples
+- Imaging events on 2023-05-02 (“liver metastasis”) and 2023-05-20 (“lung mets”) →
+  both events’ `secondary_cancer_type` become: "liver metastasis, lung metastasis".
+- Physician notes on 2023-06-01 and 2023-06-25 with only one mentioning “brain mets” →
+  both physician events get: "brain metastasis".
+
+
 
 You are an expert oncology clinical data curator. Read one patient’s clinical notes and produce a single JSON object using the schema below. Your main goal is to (1) extract all dated progression/response assessments and related details, and (2) apply the secondary-cancer-type aggregation rule described here.