Zauron Guardian Pro

One model_portfolio row per model (zauron_id). If a row already exists, save does nothing. Optionally pick a procedure type to seed CPT / modality / body part.

Select one or more ICD-10 codes (each must exist in exam_diagnosis). The server creates one new portfolio row per code, copying CPT, modality, body part, thresholds, flags, exclusions, and age gates from the source row. Duplicate rows are skipped with a summary.

Counts reflect the selected model, ICD code, date range, and optional modality / body-part / CPT filters. Confidences are read from ai_detection_event.conf (not the detection view).

Cohort

Distinct exams that have at least one ai_detection_event row for the chosen zauron_id, with exam_event.datetime_processed inside the selected range (and matching procedure filters when set).

AI predicted positive vs negative

For the selected ICD, consider all detection rows for that exam and model. Let the maximum confidence for that ICD be max_conf. The exam is AI-positive if max_conf ≥ current Prediction Threshold; if there is no row for that ICD, max_conf is treated as absent and the exam is AI-negative.

Actual positive vs negative (reference standard)

Actual-positive if the radiologist assigned that ICD on the exam in exam_event_diagnosis, or any ai_detection_event row for this model and ICD has discrepancy_override true (discrepancy agent affirmed the finding is documented in the report, including equivalent narrative). Otherwise actual-negative.

Cells

• True positive (TP): AI-positive and actual-positive.
• False negative (FN): AI-negative and actual-positive.
• False positive (FP): AI-positive and actual-negative.
• True negative (TN): AI-negative and actual-negative.