Problem Oriented Diagnostic Service for Describing Clinical Cases based on the GraphQL POMR Approach
Bibliographic record
Abstract
Since late 1960s when Dr. Larry Weed painted the roadmap to the formal process of medical diagnosis and the way it should structured around problem list based on what he calls SOAP note (Subjective, Objective, Assessment and the Plan), no real implementation or integration of his vision in providing the 'glue' to link the number of required layers, including the SOAP structuring, semantics of assessments and workflows, clinical decision support systems logic, task planning and querying each case based on flexible schema. Diagnosis, as Weed put it, a process that can describe a clear, logical and systematic approach for clinical diagnosis based on Weed's approach when a patient encountered a clinical issue. This process need to be modeled a service that can integrate with the other clinical systems and services including the medical record systems. In this article we are proposing such service based on Weeds approach for the clinical diagnosis purposes. The described service starts with a SOAP schema that utilizes the GraphQL standard and its associated toolkits to offer physicians and pre-clerkships the mean for describing diagnosis of clinical cases according to the Weed's approach. Particular attention has been given to processing and refining of the of SOAP diagnosis note through primitives like create, retrieve, update and delete (CRUD).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".