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README.md

@@ -14,8 +14,7 @@ Predicts which of 40 target conditions are **newly** diagnosed in the five
 years after a patient's anchor date, from structured Synthea EHR events
 recorded strictly before it.
 
-Trained for the M31 research-intern take-home. **Synthetic data only. Not a
-clinical device, and not usable for any decision about a real person.**
+Trained for the M31 research-intern take-home, on synthetic data only.
 
 ## Model
 
@@ -74,20 +73,19 @@ shift. Every fitted statistic — vocabulary, quantile edges, scalers — is fit
 on training patients only. 152 automated checks cover this, including a grep
 test that no module outside the time utility parses a timestamp.
 
-## Limitations
-
-Synthea is not real EHR data. A discriminator separates it from MIMIC at
-AUC 0.999, and across 19 health datasets the winning classifier agreed between
-real- and synthetic-trained models in only 21–26% of cases. **Whichever model
-wins here, that ranking should not be assumed to transfer.**
-
 ## Reproducing
 
-```bash
-git clone https://github.com/Sallamsaka/M31-Coding-Test && cd M31-Coding-Test
-pip install -r requirements.txt
-# put the provided data (train_val/, test/, the three CSVs) in the repo root, then:
-python -m src.reproduce --from-hub sallamsaka/M31-Coding-Test   # predictions from these files, no training
-```
+The data is not included here. Rebuilding `predictions.csv` from the files in
+this repository, without any training:
+
+1. Clone the code and install it:
+   `git clone https://github.com/Sallamsaka/M31-Coding-Test`, then
+   `pip install -r requirements.txt` inside it.
+2. Copy the provided data into the repository root: the folders `train_val/` and
+   `test/`, and the files `patient_splits.csv`, `target_conditions.csv` and
+   `test_anchors.csv`.
+3. Run `python -m src.reproduce --from-hub sallamsaka/M31-Coding-Test`. It downloads
+   these model files, predicts every test patient, and checks the result against
+   the submitted `predictions.csv`.
 
 Retraining everything from scratch is described in the GitHub README.