In catastrophic injury cases, the “value” of a settlement is driven less by the label of the diagnosis and more by what the record proves: the injury mechanism, neurological findings, and the life-care needs that follow.
AI tools often use simplified inputs (injury severity, age, time to recovery) to generate a range. The problem is that spinal cord injuries vary widely in the real world—especially when the medical file is incomplete, misunderstood, or heavily contested.
In Kendallville, adjusters commonly look for gaps such as:
- Unclear timing between the crash/work incident and the first neurological complaints
- Conflicting accounts about how the injury happened (even minor inconsistencies)
- Missing imaging and functional testing in the early documentation
- Unverified future care plans (therapy frequency, equipment, home support)
When these issues exist, an AI-generated figure can be off in either direction.


