Ontology Engineering to Model Clinical Pathways: Towards the Computerization and Execution of Clinical Pathways
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Clinical pathways translate evidence-based recommendations into locally practicable, process-specific algorithms that reduce practice variations and optimize quality of care. Our objective was to abstract practice-oriented knowledge from a cohort of real clinical pathways and represent this knowledge as a clinical pathway ontology. We employed a four step methodology: (1) knowledge source identification and classification of clinical pathways according to variations in setting, stage of care, patient type, outcome and specialty; (2) iterative knowledge abstraction using grounded theory; (3) ontology engineering as adapted from the Model-based Incremental Knowledge Engineering approach; and, (4) ontology evaluation through encoding a sample of real clinical pathways. We present our clinical pathway ontology that offers a detailed ontological model describing the structure and function of clinical pathways. Our ontology can potentially integrate with a healthcare semantic web, and ontologies for clinical practice guidelines, patients and institutions to form the foundational knowledge for generating patient-specific CarePlans.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it