Table 1:

Translational NLP checklist items for Disability Review case study, including notes on published results.

Information Need	Overall goal: Improve disability benefits review process by highlighting relevant information Formal representation: Spans of evidence, with attributes for activity type and level of limitation
Data Characteristics	Medical records and administrative forms from USA, mostly English
Task Paradigms	Information extraction (spans), information retrieval (documents), span classification (activity and limitations)
Available Resources	Minimal knowledge sources for function and disability, no public corpora; US government computing systems; high throughput requirements (thousands of records/day)
NLP Technologies	Low-latency, low-compute sequence models; rule-based systems
Evaluation	Standard metrics (F-1, accuracy). Information retrieval metrics reported for use case prototypes.
Interpretation	Interpretation needs primarily around human decision-making; NLP tools highlight and organize information in context. No ML interpretability reported in published results.
Application Engineering	Open-source implementations using standardized frameworks for preprocessing. No data exchange reported.