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Causation & Apportionment Graph

From injury event to today, every link in the chain, cited. BETA

Maps the causal chain from injury event through symptoms, imaging, treatment, and prior conditions to alternative causes, a structured, cited apportionment view, not a conclusion.

Adams, T. · Case #IME-4812 Right knee
Injury eventp.12
Symptom onsetp.28
Imaging findingp.140
Prior right-knee injury (2020)p.318
Treatment coursep.204
Structured chain · every link cited to its source page
IN ACTIVE BETA · refined with early customers
Every node on the chain traces back to a source page, so the graph shows what the record supports, not what it concludes.

The chain, laid out node by node.

Injury event, symptom onset, imaging, treatment, and outcome are mapped as a connected chain rather than a scattered set of dates. Each node is drawn directly from the record, in the order it happened.

Injury, symptoms, imaging, and treatment mapped in sequence
Built from the record, not from an assumed narrative
Chain nodesRight knee
Injury eventp.12
Symptom onsetp.28
Imagingp.140
Treatment coursep.204
Competing pathways2 flagged
Prior right-knee injury (2020)p.318
Degenerative finding, unrelated visitp.322

Prior conditions and alternative causes, shown not buried.

Pre-existing conditions and competing causes surfaced elsewhere in the file are placed alongside the primary chain instead of left for a reviewer to find on their own, each with its own citation.

Prior conditions surfaced next to the primary chain
Competing causes cited, not omitted

Every link cited to its source page.

Click any node on the chain and jump straight to the page it came from. Nothing on the graph is inferred without a citation attached, so the chain can be checked link by link.

Every node links to its exact source page
No node appears without a citation
Citation check4 of 4 linked
Injury event → p.12Verified
Imaging → p.140Verified
Neutral workspaceCase #IME-4812
Chain mapped 4 nodes
Competing causes shown 2 flagged
Apportionment % Not assigned
Causation opinion Examiner's own
The boundary

A structured chain, never a conclusion.

The Causation & Apportionment Graph shows the causal chain and any competing pathways it finds in the record. It never assigns a percentage apportionment and never states a causation opinion on its own.

The evaluator interprets the chain and decides what it means for the case. The graph's job stops at laying out the cited evidence in order, not at drawing the conclusion from it.

From scattered dates to one cited chain.

Three steps, every link traced back to its page.

1. Extract chain events

Injury, symptoms, imaging, treatment, and prior conditions are pulled from the record and dated.

2. Map the links & flag alternatives

Events are connected in sequence, and competing pathways or prior conditions are flagged alongside the primary chain.

3. Present the cited graph

The evaluator reviews the structured, cited chain and forms the causation opinion themselves.

Built for anyone who has to argue causation.

Same cited chain, read for different purposes.

FAQ

Frequently asked questions.

It's a structured, cited map of the causal chain in a case, from the injury event through symptoms, imaging, treatment, and prior conditions to any alternative causes in the record.

No. It shows the causal chain and any competing pathways, cited to the record. It never assigns a percentage or states a causation opinion; the evaluator interprets the chain.

Yes, in beta. The Causation & Apportionment Graph is live and testable now; we're refining it hands-on with early customers, and if your use case is a good fit we'll work with you directly.

They're surfaced alongside the primary chain rather than buried in the file, each cited to its source page, so the evaluator sees the full picture before forming an opinion.

Yes. Every node on the graph links to the exact page it was drawn from, so the chain can be checked link by link.

Related to Causation & Apportionment Graph.

See the chain, cited, before you write the opinion.

Join the beta and map the causal chain on a file of your own, or book a demo to see it built live. Handled under our BAA; never used to train a model.