GraphQL Patient Case Presentation using the Problem Oriented Medical Record Schema
Bibliographic record
Abstract
Physicians and Clerkships follow the SOAP note in describing and presenting patient cases. The SOAP note was originated from the problem-oriented medical record (POMR) developed nearly 60 years ago by Lawrence Weed, MD. However, the POMR/SOAP is not commonly found in electronic medical records (EMR) used today due to the flexible nature of building patient cases that requires complex harmonization with variety of patient case schemas. In this article we attempted to use the GraphQL API for harmonizing patient case data communicated with different care interface and providing the query on the data. This harmonization interface is called QL4POMR as a GraphQL implementation to the POMR SOAP note. Physicians can used this interface to describe and present any patient case for the purpose of diagnosis and prognosis with a varying backend. The QL4POMR implemented a mapping module to map graphs from POMR to HL7 FHIR and vise versa.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".