Update README.md

README.md

@@ -16,9 +16,11 @@ metrics:
 We used GPT4.1 to extract a binary macro-vascular-disease label from more than 7000 Dutch CT Heart reports 
 in the University Medical Center Utrecht. 
 
-In parallel we developed a heuristics pipeline based on $$CADRADS>2$$, $$MESA>65\%$$, $$CACS>400$$ 
-and a $$stenosis/occlusion\geq 50\%$$, together with regular expressions regarding the macro-vascular disease level.
+In parallel we developed a heuristics pipeline based on CADRADS>2, MESA>65%, CACS>400 
+and a stenosis/occlusion>=50%, together with regular expressions regarding the macro-vascular disease level.
 
-This model is trained on the labels extracted with GPT-4.1, these labels were consistent with the heuristics-based labeling pipeline.
+This model is trained on the labels extracted with GPT-4.1, these labels were consistent with the heuristics-based labeling pipeline, with a correlation score of ahout 
+about 0.83(Pearson).
 
-In 10-fold cross-validation the model scored f1,precision and recall of about $$93\%$$.
+In 10-fold cross-validation the model scored f1,precision and recall of about 93%, the model uploaded here was 1 fold from a 40-fold split, and obtained 90% (f1,prec,rec).
+Note: this scoring was obtained with the stand 0.5 proba threshold.