Most AI-style tools work by taking a few inputs (injury severity, treatment, and losses) and applying broad averages. In real Oroville cases, outcomes hinge on details that averages can’t reliably capture, such as:
- Comparative fault arguments (California uses comparative negligence, so insurers often try to reduce what they pay by claiming partial fault)
- Causation disputes (insurers may argue symptoms are unrelated or pre-existing)
- Trucking-specific evidence gaps (maintenance history, driver logs, cargo/inspection documentation)
- Local timing issues (some records take time to obtain, and delays can affect early settlement leverage)
Instead of treating a calculator as a prediction, think of it as a starting checklist—then we build the actual case value around proof.


