Overview | Step 1: Pre-linkage audit with preflight() | Step 2: Medicare checksum validation with check_medicare() | Step 3: Blocking variable construction with flock() | Step 4: Threshold sensitivity analysis with perch() | Step 5: Probabilistic linkage with murmuration() | Step 6: Visualise the score distribution with murmuration_plot() | Step 7: Post-linkage validation | Summary: the complete starling workflow | Session information

Why pre-linkage quality matters | preflight() — structured pre-linkage audit | check_medicare() — Modulus 10 checksum validation | What to do with invalid Medicare numbers | flock() — blocking variable construction | Choosing blocking variables for Australian datasets | Putting it together | Session information

Why the threshold matters | Australian linkage authority benchmarks | Example: running perch() on scored pairs | Example: visualising the score distribution | Using perch_before_linking = TRUE inside murmuration() | Clerical review zone | Session information