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T G Hoff

Publications and source records attributed to T G Hoff.

2 recordsLinked to original sources

Intraspinal cross-section areas measured on myelography--computed tomography. The relation to outcome in nonoperated lumbar disc herniation.

STUDY DESIGN: A prospective cohort study with computed tomographic-myelographic and clinical base registrations and follow-up by questionnaire 14 months after the onset of leg pain. OBJECTIVES: To detect predictive relations between intraspinal area measures and outcome in nonoperated lumbar disc herniation. SUMMARY OF BACKGROUND DATA: Myelography and computed tomography are qualitative methods ordinarily. The authors wanted to investigate the quantitative possibilities of computed tomographic technology in the pursuit of predictive measures. METHOD: Cross-section areas of the disc hernia, the dural sac, and the residual spinal canal were measured on computed tomography-myelography in 58 patients with lumbar disc herniation who did not undergo surgery. Pain, disability, and vocational status were asked for in a questionnaire after 8 months. RESULTS: After a median of 14 months from the onset of leg pain, 77% had returned to work, and only 7% were pain-free. The hernia size was not associated with the outcome measures. A high score for pain intensity and distal pain distribution was associated with a wide dural sac and a wide residual spinal canal. Disability and vocational status were not associated with any of the areas measured. Patients with the longest duration of leg pain had the narrowest spinal canals. CONCLUSIONS: Area measurements on computed tomography-myelography could not predict outcome in patients with lumbar disc herniation who were not operated on. There was an association between pain and a wide dural sac, which might underscore the sensory role of dura in protracted cases of sciatica.

Adult↗

Representing hospital events as complex conditionals.

We have developed an approach to medical knowledge representation whereby simple medical concepts are combined to yield complex statements of testable medical logic. The logic is created from a small number of generic medical concepts that are instantiated and combined to create the rules. Rule writing is done through a rule editor and requires knowledge of the system's data dictionaries, though no programming is required. We have used the approach to create a large knowledge base including panic lab alerting rules, drug-laboratory interaction alerting rules, an adverse drug event monitor, and a drug-age interaction detection program. The rules have been used as part of an alerting system and for data collection to determine the frequency of events of interest. The scheme is extensible and yields a readable form of the created knowledge. The scheme holds great promise as a durable form of medical knowledge representation.

Artificial Intelligence↗