A methodology to create Complex Adaptive System models that support Cardiovascular Diseases simulation
Bibliographic record
Abstract
This paper describes a methodology for creating a Complex Adaptive System (CAS) computer model that supports assessments of Cardiovascular Diseases (CVD) over a period of time. The Agent Based Model (ABM) was implemented in NetLogo and allowed for the complex interdependency of the risk factors and feedback loops from the health interventions at different levels. The CVD assessments are normally based on mathematical equations, predictive risk algorithms or the World Health Organization/International Society of Hypertension (WHO/ISH) predication charts. The 10 year WHO/ISH risk score charts are particularly important because they are calibrated for low and middle income countries unlike the popular Framingham Risk Score and the Systematic Coronary Risk Evaluation (SCORE). WHO/ISH charts contain risk factors that are easier to maintain as a part of medical records of persons in low and middle income countries. The Framingham Risk Score and SCORE were developed and validated in high income countries predominantly with Caucasian populations. Steps to create the model based on WHO/ISH prediction charts are described. The model is applicable to low and middle income countries which account for 80% of CVD related deaths globally.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".