Investigation into Scaling-Up the SOAP Problem-Oriented Medical Record into a Clinical Case Study
Bibliographic record
Abstract
Modern clinical decision making is increasingly based on evidence-based practice. This practice provides healthcare professionals as well as patients with scientifically proven information on the various medical options that are available to them. Lawrence Weed (MD) since early 1960 provided a vision that can make clinical practice more evidence-based by linking everything at the bedside and the bench side through a problem list. Physicians need to describe their patient cases based on the SOAP note needs to be linked to the problem list as well as experts at the bench they need to link things like drugs to the problem list. According to this vision, we managed to build our QL4POMR during the last five years; however, in this article we are presenting our initial investigation into how to scale our prototype so it can enable clinicians to describe clinical cases with the ability to interrogate medical repositories for more information to arrive at a better diagnosis, prognosis or building a clinical case report. This paper investigate scaling the SOAP note into a clinical study report based on using the PICO framework to generate clinical questions related to the patient(s) case and compile a clinical report based on the outcome of the search to answer these questions.
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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 | 0.027 | 0.084 |
| 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 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".